Nigam_TCGA_KIRP_KidneyCancer_SLC22_Analysis.ipynb

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     "text": [
      "also installing the dependencies ‘bit’, ‘bit64’, ‘clipr’, ‘vroom’, ‘tzdb’, ‘rematch’, ‘prettyunits’, ‘matrixStats’, ‘RcppArmadillo’, ‘forcats’, ‘hms’, ‘readr’, ‘cellranger’, ‘progress’, ‘zip’, ‘numDeriv’, ‘SparseM’, ‘MatrixModels’, ‘conquer’, ‘sp’, ‘foreign’, ‘haven’, ‘curl’, ‘readxl’, ‘openxlsx’, ‘boot’, ‘minqa’, ‘nloptr’, ‘RcppEigen’, ‘carData’, ‘abind’, ‘nnet’, ‘pbkrtest’, ‘quantreg’, ‘maptools’, ‘rio’, ‘lme4’, ‘mime’, ‘bitops’, ‘nlme’, ‘Rcpp’, ‘corrplot’, ‘car’, ‘colorspace’, ‘lattice’, ‘stringi’, ‘highr’, ‘markdown’, ‘yaml’, ‘xfun’, ‘RCurl’, ‘png’, ‘jpeg’, ‘xml2’, ‘gtable’, ‘isoband’, ‘MASS’, ‘mgcv’, ‘withr’, ‘ggrepel’, ‘ggsci’, ‘cowplot’, ‘ggsignif’, ‘polynom’, ‘rstatix’, ‘exactRankTests’, ‘mvtnorm’, ‘farver’, ‘labeling’, ‘munsell’, ‘R6’, ‘RColorBrewer’, ‘viridisLite’, ‘Matrix’, ‘backports’, ‘generics’, ‘stringr’, ‘tidyselect’, ‘cpp11’, ‘knitr’, ‘KMsurv’, ‘data.table’, ‘zoo’, ‘km.ci’, ‘xtable’, ‘pkgconfig’, ‘gridtext’, ‘ggplot2’, ‘ggpubr’, ‘gridExtra’, ‘magrittr’, ‘maxstat’, ‘scales’, ‘survival’, ‘broom’, ‘dplyr’, ‘tidyr’, ‘survMisc’, ‘purrr’, ‘tibble’, ‘ggtext’\n",
      "\n",
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      "“installation of package ‘survMisc’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘ggtext’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘broom’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘pbkrtest’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘rio’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘car’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘rstatix’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘ggpubr’ had non-zero exit status”\n",
      "Warning message in install.packages(\"survminer\"):\n",
      "“installation of package ‘survminer’ had non-zero exit status”\n",
      "Updating HTML index of packages in '.Library'\n",
      "\n",
      "Making 'packages.html' ...\n",
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    "#install.packages('survminer')"
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   "execution_count": 1,
   "id": "ultimate-cement",
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     "name": "stderr",
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     "text": [
      "Loading required package: AnnotationDbi\n",
      "\n",
      "Loading required package: stats4\n",
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      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
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      "Welcome to Bioconductor\n",
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      "\n",
      "    lowess\n",
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   ],
   "source": [
    "library(org.Hs.eg.db)\n",
    "library(limma)\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(ggpubr)\n",
    "library(survival)\n",
    "library(survminer)\n",
    "library(gplots)\n",
    "EG2SYM <- toTable(org.Hs.egSYMBOL)\n",
    "extra_anno <- data.frame(gene_id=c(6890, 6891, 883, 56267, 7067, 7068),\n",
    "                         symbol=c(\"ABCB2\", \"ABCB3\", \"CCBL1\", \"CCBL2\", \"THRCA\", \"THRCB\"))\n",
    "EG2SYM <- rbind(EG2SYM, extra_anno)\n",
    "\n",
    "ENS2EG <- toTable(org.Hs.egENSEMBL2EG)\n",
    "ENS2EG2SYM <- cbind.data.frame(EG2SYM, ENS2EG[match(EG2SYM$gene_id, ENS2EG$gene_id),])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "controversial-python",
   "metadata": {},
   "outputs": [],
   "source": [
    "brin_ADME_nodes <- read.csv(file=\"./data/Rosenthal_2019Pub_ADME List.txt\", sep=\"\\t\", header=FALSE)\n",
    "colnames(brin_ADME_nodes) <- \"Sym\"\n",
    "brin_ADME_nodes$EGID <- ENS2EG2SYM$gene_id[match(brin_ADME_nodes$Sym, ENS2EG2SYM$symbol)]\n",
    "brin_ADME_nodes$ENSID <- ENS2EG2SYM$ensembl_id[match(brin_ADME_nodes$Sym, ENS2EG2SYM$symbol)]\n",
    "brin_ADME_nodes <- rbind.data.frame(brin_ADME_nodes[!is.na(brin_ADME_nodes$EGID),], \n",
    "                                    data.frame(Sym=c(\"CCN1\",\"SLC49A4\", \"SLC49A3\", \"SLC35E2A\", \"NAA80\"),\n",
    "                                               EGID=c(\"3491\", \"84925\",\"84179\", \"9906\", \"24142\"),\n",
    "                                               ENSID=c(\"ENSG00000142871\", \"ENSG00000138463\", \"ENSG00000169026\", \n",
    "                                                       \"ENSG00000215790\", \"ENSG00000243477\"))) \n",
    "\n",
    "clin_data <- read.csv(file=\"./data/TCGA_RNASeq/clinical.tsv\", \n",
    "                      sep=\"\\t\", header=TRUE, stringsAsFactors = FALSE)\n",
    "clin_data3 <- clin_data[!duplicated(clin_data[,-c(152:154)]),]\n",
    "clin_data_filt <- clin_data3[grep(\"TCGA\", clin_data3$project_id),]\n",
    "clin_data.l <- split(clin_data_filt, clin_data_filt$project_id)\n",
    "all_SLC22_nodes.df <- all_SLC22_nodes_orig.df <- EG2SYM[grep(\"SLC22A\", EG2SYM$symbol),]\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "narrative-coordination",
   "metadata": {},
   "outputs": [
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8bevXs1Go0QYs+ePcOHDx83bpwQorm5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\nUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Color Key”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tum_Norm_KIRC_KIRP <- read.table(file=\"./data/TCGA_KIRC_KIRP_TumorNormalSLC22geneExp.txt\", sep=\"\\t\", header=TRUE)\n",
    "Tum_Norm_KIRC <- Tum_Norm_KIRC_KIRP[,2:3]\n",
    "Tum_Norm_KIRP <- Tum_Norm_KIRC_KIRP[,5:6]\n",
    "rownames(Tum_Norm_KIRC) <- rownames(Tum_Norm_KIRP) <- Tum_Norm_KIRC_KIRP[,1]\n",
    "mycol = colorpanel(1000, \"#4B617D\", \"white\", \"#a63a45\")\n",
    "#pdf(file=\"Nigam_KIRP_SLC22_TumorNormalHeatmap_06252021.pdf\", width=7, height=8)\n",
    "#png(file=\"Nigam_KIRP_SLC22_TumorNormalHeatmap_06252021.png\", width=7, height=8, res=300, units=\"in\")\n",
    "heatmap.2(as.matrix(Tum_Norm_KIRP), dendrogram=\"row\", density.info=\"none\", trace=\"none\",\n",
    "          labCol=c(\"Normal\", \"Tumor\"), col=mycol, margin=c(8,8), lhei=c(2,10), cexCol=1.5)\n",
    "#dev.off()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifty-football",
   "metadata": {},
   "source": [
    "## KIRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "global-fifteen",
   "metadata": {},
   "outputs": [],
   "source": [
    "KIRP_data <- read.table(file=\"./data/TCGA_RNASeq/KIRP.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt\", \n",
    "                        sep=\"\\t\", header=TRUE, stringsAsFactors = FALSE)\n",
    "KIRP_data_rows <- KIRP_data[,1]\n",
    "KIRP_data_rows_filt <- gsub(\"^\\\\?\\\\|\",\"\", KIRP_data_rows)\n",
    "KIRP_data_rows_filt2 <- cbind.data.frame(symbol=unlist(lapply(strsplit(KIRP_data_rows_filt[-1], split=\"\\\\|\"), \n",
    "                                                              function(x) ifelse(length(x) > 1, x[[1]], NA))), \n",
    "                                         gene_id=unlist(lapply(strsplit(KIRP_data_rows_filt[-1], split=\"\\\\|\"), \n",
    "                                                               function(x) ifelse(length(x) > 1, x[[2]], x[[1]]))))\n",
    "\n",
    "KIRP_sample_names <- colnames(KIRP_data[,-1])\n",
    "KIRP_sample_names_filt <- unlist(lapply(strsplit(KIRP_sample_names, split=\"\\\\.\"), function(x) {\n",
    "  paste(x[[1]], x[[2]], x[[3]], sep=\"-\")\n",
    "  }))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "lovely-browse",
   "metadata": {},
   "outputs": [],
   "source": [
    "KIRP_data_tumor <- KIRP_data[-1,-1][, !unlist(lapply(strsplit(KIRP_sample_names, split=\"\\\\.\"), function(x) x[[4]])) %in% c(\"11A\",\"05A\")]\n",
    "\n",
    "KIRP_data_tumor <- as.data.frame(apply(KIRP_data_tumor, 2, as.numeric))\n",
    "rownames(KIRP_data_tumor) <- as.character(KIRP_data_rows_filt2$gene_id)\n",
    "KIRP_data_tumor_rs <- rowSums(KIRP_data_tumor>75)\n",
    "KIRP_data_tumor_thr <- ncol(KIRP_data_tumor)/2\n",
    "KIRP_data_tumor_thrfilt <- KIRP_data_tumor[which(KIRP_data_tumor_rs > KIRP_data_tumor_thr),]\n",
    "KIRP_data_rows_filt2b <- KIRP_data_rows_filt2[which(KIRP_data_tumor_rs > KIRP_data_tumor_thr),]\n",
    "KIRP_data_tumor_log <- apply(KIRP_data_tumor_thrfilt, 2, function(x) log2(as.numeric(x) + 0.0001))\n",
    "rownames(KIRP_data_tumor_log) <- rownames(KIRP_data_tumor_thrfilt)\n",
    "\n",
    "KIRP_tumor_sample_names <- colnames(KIRP_data_tumor_log)\n",
    "KIRP_tumor_sample_names_filt <- unlist(lapply(strsplit(KIRP_tumor_sample_names, split=\"\\\\.\"), function(x) {\n",
    "  paste(x[[1]], x[[2]], x[[3]], sep=\"-\")\n",
    "}))\n",
    "                             \n",
    "KIRP_data_tumor_filt <- KIRP_data_tumor_thrfilt[,KIRP_tumor_sample_names_filt %in% clin_data3$case_submitter_id]\n",
    "rownames(KIRP_data_tumor_filt) <- KIRP_data_rows_filt2b$gene_id\n",
    "KIRP_tumor_filt_sample_names <- colnames(KIRP_data_tumor_filt)\n",
    "KIRP_tumor_filt_sample_names_filt <- unlist(lapply(strsplit(KIRP_tumor_filt_sample_names, split=\"\\\\.\"), function(x) {\n",
    "  paste(x[[1]], x[[2]], x[[3]], sep=\"-\")\n",
    "}))\n",
    "                             \n",
    "KIRP_data_tumor_rs_orig <- rowSums(KIRP_data_tumor>01)\n",
    "KIRP_data_rows_filt_orig <- KIRP_data_rows_filt2[which(KIRP_data_tumor_rs_orig > KIRP_data_tumor_thr),]\n",
    "KIRP_data_tumor_thrfilt_orig <- KIRP_data_tumor[which(KIRP_data_tumor_rs_orig > KIRP_data_tumor_thr),]\n",
    "KIRP_data_tumor_filt_orig <- KIRP_data_tumor_thrfilt_orig[,KIRP_tumor_sample_names_filt %in% clin_data3$case_submitter_id]\n",
    "rownames(KIRP_data_tumor_filt_orig) <- KIRP_data_rows_filt_orig$gene_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "pleasant-temperature",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in eval(expr, envir, enclos):\n",
      "“NAs introduced by coercion”\n"
     ]
    }
   ],
   "source": [
    "KIRP_clin_data_tumor_filt <- clin_data3[match(KIRP_tumor_filt_sample_names_filt, clin_data3$case_submitter_id),]\n",
    "KIRP_clin_data_tumor_filt$ajcc_pathologic_m <- factor(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_m),\n",
    "                                                      levels=c(\"'--\", \"MX\", \"M0\", \"M1\"))\n",
    "KIRP_clin_data_tumor_filt$ajcc_pathologic_n <- factor(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_n),\n",
    "                                                      levels=c(\"'--\", \"NX\", \"N0\", \"N1\"))\n",
    "KIRP_clin_data_tumor_filt$ajcc_pathologic_stage <- factor(gsub(\" \", \"\", as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage)),\n",
    "                                                          levels=c(\"'--\", \"StageI\", \"StageII\", \"StageIII\", \"StageIV\"))\n",
    "KIRP_clin_data_tumor_filt$ajcc_pathologic_t <- factor(gsub(\"T3c\", \"T4\", as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_t)),\n",
    "                                                      levels=c(\"TX\", \"T1\", \"T1a\",\"T1b\", \"T2\", \"T2a\", \"T2b\",\n",
    "                                                               \"T3\", \"T3a\",\"T3b\", \"T3c\",\"T4\"))\n",
    "KIRP_clin_data_tumor_filt$race <- factor(gsub(\" \", \"_\", gsub(\"american indian or alaska native\", \"indig\", \n",
    "                                                             gsub(\"black or african american\", \"black\", \n",
    "                                                                  as.character(KIRP_clin_data_tumor_filt$race)))), \n",
    "                                         levels=c(\"indig\", \"asian\", \"black\", \"not_reported\", \"white\"))\n",
    "KIRP_clin_data_tumor_filt$gender <- factor(as.character(KIRP_clin_data_tumor_filt$gender), \n",
    "                                         levels=c(\"male\", \"female\"))\n",
    "KIRP_clin_data_tumor_filt$age_at_index <- as.numeric(KIRP_clin_data_tumor_filt$age_at_index)\n",
    "KIRP_data_tumor_filt_m <- KIRP_data_tumor_filt[,!KIRP_clin_data_tumor_filt$ajcc_pathologic_m %in% c(NA, \"'--\")]\n",
    "KIRP_clin_data_tumor_filt_m <- subset(KIRP_clin_data_tumor_filt, ajcc_pathologic_m != \"'--\")\n",
    "KIRP_clin_data_tumor_filt_m <- droplevels(KIRP_clin_data_tumor_filt_m)\n",
    "KIRP_design_m <- model.matrix(~0 + KIRP_clin_data_tumor_filt_m$ajcc_pathologic_m)\n",
    "KIRP_voom_m <- KIRP_voom <- voom(as.matrix(KIRP_data_tumor_filt_m), KIRP_design_m, plot=FALSE)\n",
    "\n",
    "KIRP_data_tumor_filt_n <- KIRP_data_tumor_filt[,!KIRP_clin_data_tumor_filt$ajcc_pathologic_n %in% c(NA, \"'--\")]\n",
    "KIRP_clin_data_tumor_filt_n <- subset(KIRP_clin_data_tumor_filt, ajcc_pathologic_n != NA | ajcc_pathologic_n != \"'--\")\n",
    "KIRP_clin_data_tumor_filt_n <- droplevels(KIRP_clin_data_tumor_filt_n)\n",
    "KIRP_design_n <- model.matrix(~0 + KIRP_clin_data_tumor_filt_n$ajcc_pathologic_n)\n",
    "KIRP_voom_n <- voom(as.matrix(KIRP_data_tumor_filt_n), KIRP_design_n, plot = FALSE)\n",
    "\n",
    "KIRP_data_tumor_filt_stage <- KIRP_data_tumor_filt[,!KIRP_clin_data_tumor_filt$ajcc_pathologic_stage %in% c(NA, \"'--\")]\n",
    "KIRP_clin_data_tumor_filt_stage <- subset(KIRP_clin_data_tumor_filt, ajcc_pathologic_stage != \"'--\" | ajcc_pathologic_stage != NA)\n",
    "KIRP_clin_data_tumor_filt_stage <- droplevels(KIRP_clin_data_tumor_filt_stage)\n",
    "KIRP_design_stage <- model.matrix(~0 + KIRP_clin_data_tumor_filt_stage$ajcc_pathologic_stage)\n",
    "KIRP_voom_stage <- voom(as.matrix(KIRP_data_tumor_filt_stage), KIRP_design_stage, plot = FALSE)\n",
    "\n",
    "KIRP_data_tumor_filt_t <- KIRP_data_tumor_filt[,!KIRP_clin_data_tumor_filt$ajcc_pathologic_t %in% c(NA, \"'--\", \"T3c\")]\n",
    "KIRP_clin_data_tumor_filt_t <- subset(KIRP_clin_data_tumor_filt, ajcc_pathologic_t != \"T3c\")\n",
    "KIRP_clin_data_tumor_filt_t <- droplevels(KIRP_clin_data_tumor_filt_t)\n",
    "KIRP_design_t <- model.matrix(~0 + KIRP_clin_data_tumor_filt_t$ajcc_pathologic_t)\n",
    "KIRP_voom_t <- voom(as.matrix(KIRP_data_tumor_filt_t), KIRP_design_t, plot = FALSE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "chemical-danish",
   "metadata": {},
   "outputs": [],
   "source": [
    "## with Race, Gender, & Age\n",
    "KIRP_filt_age <- which(is.na(KIRP_clin_data_tumor_filt$age_at_index))\n",
    "KIRP_data_tumor_filt_age <- KIRP_data_tumor_filt[,-KIRP_filt_age]\n",
    "KIRP_clin_data_tumor_filt_age <- KIRP_clin_data_tumor_filt[-KIRP_filt_age,]\n",
    "\n",
    "KIRP_data_tumor_filt_age <- KIRP_data_tumor_filt_age\n",
    "KIRP_clin_data_tumor_filt_race_age_gen <- KIRP_clin_data_tumor_filt_age\n",
    "KIRP_clin_data_tumor_filt_race_age_gen <- droplevels(KIRP_clin_data_tumor_filt_race_age_gen)\n",
    "KIRP_design_race_age_gen <- model.matrix(~0 + race + age_at_index + gender, \n",
    "                                   data=KIRP_clin_data_tumor_filt_race_age_gen)\n",
    "KIRP_voom_race_age_gen <- voom(as.matrix(KIRP_data_tumor_filt_age), KIRP_design_race_age_gen, plot=FALSE)\n",
    "\n",
    "\n",
    "KIRP_filt_all_m <- unique(c(which(KIRP_clin_data_tumor_filt$ajcc_pathologic_m %in% c(NA, \"'--\")), \n",
    "                            which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_all_m <- KIRP_data_tumor_filt[,-KIRP_filt_all_m]\n",
    "KIRP_clin_data_tumor_filt_all_m <-KIRP_clin_data_tumor_filt[-KIRP_filt_all_m,]\n",
    "KIRP_clin_data_tumor_filt_all_m <- droplevels(KIRP_clin_data_tumor_filt_all_m)\n",
    "KIRP_design_all_m <- model.matrix(~0 + ajcc_pathologic_m + age_at_index + gender + race, data=KIRP_clin_data_tumor_filt_all_m)\n",
    "KIRP_voom_all_m <- voom(as.matrix(KIRP_data_tumor_filt_all_m), KIRP_design_all_m)\n",
    "\n",
    "KIRP_filt_all_n <- unique(c(which(KIRP_clin_data_tumor_filt$ajcc_pathologic_n %in% c(NA, \"'--\")), \n",
    "                            which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_all_n <- KIRP_data_tumor_filt[,-KIRP_filt_all_n]\n",
    "KIRP_clin_data_tumor_filt_all_n <-KIRP_clin_data_tumor_filt[-KIRP_filt_all_n,]\n",
    "KIRP_clin_data_tumor_filt_all_n <- droplevels(KIRP_clin_data_tumor_filt_all_n)\n",
    "KIRP_design_all_n <- model.matrix(~0 + ajcc_pathologic_n + age_at_index + gender + race, data=KIRP_clin_data_tumor_filt_all_n)\n",
    "KIRP_voom_all_n <- voom(as.matrix(KIRP_data_tumor_filt_all_n), KIRP_design_all_n)\n",
    "\n",
    "KIRP_filt_all_stage <- unique(c(which(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage %in% c(NA, \"'--\")), \n",
    "                            which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_all_stage <- KIRP_data_tumor_filt[,-KIRP_filt_all_stage]\n",
    "KIRP_clin_data_tumor_filt_all_stage <-KIRP_clin_data_tumor_filt[-KIRP_filt_all_stage,]\n",
    "KIRP_clin_data_tumor_filt_all_stage <- droplevels(KIRP_clin_data_tumor_filt_all_stage)\n",
    "KIRP_design_all_stage <- model.matrix(~0 + ajcc_pathologic_stage + age_at_index + gender + race, data=KIRP_clin_data_tumor_filt_all_stage)\n",
    "KIRP_voom_all_stage <- voom(as.matrix(KIRP_data_tumor_filt_all_stage), KIRP_design_all_stage)\n",
    "\n",
    "KIRP_filt_all_t <- unique(c(which(KIRP_clin_data_tumor_filt$ajcc_pathologic_t %in% c(NA, \"'--\", \"T3C\")), \n",
    "                            which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_all_t <- KIRP_data_tumor_filt[,-KIRP_filt_all_t]\n",
    "KIRP_clin_data_tumor_filt_all_t <-KIRP_clin_data_tumor_filt[-KIRP_filt_all_t,]\n",
    "KIRP_clin_data_tumor_filt_all_t <- droplevels(KIRP_clin_data_tumor_filt_all_t)\n",
    "KIRP_design_all_t <- model.matrix(~0 + ajcc_pathologic_t + age_at_index, data=KIRP_clin_data_tumor_filt_all_t)\n",
    "KIRP_voom_all_t <- voom(as.matrix(KIRP_data_tumor_filt_all_t), KIRP_design_all_t)\n",
    "\n",
    "KIRP_filt_race_age_gen_tx <- unique(c(KIRP_clin_data_tumor_filt$treatment_type %in% c(NA, \"'--\"),\n",
    "        which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_tx <- KIRP_data_tumor_filt[,-KIRP_filt_race_age_gen_tx]\n",
    "KIRP_clin_data_tumor_filt_tx <- KIRP_clin_data_tumor_filt[-KIRP_filt_race_age_gen_tx,]\n",
    "KIRP_design_race_age_gen_tx <- model.matrix(~0 + treatment_type + race + age_at_index + gender, \n",
    "                                            data=KIRP_clin_data_tumor_filt_tx)\n",
    "KIRP_voom_race_age_gen_tx <- voom(as.matrix(KIRP_data_tumor_filt_tx), KIRP_design_race_age_gen_tx, plot=FALSE)\n",
    "\n",
    "KIRP_filt_race_age_gen_priMalig <- unique(c(KIRP_clin_data_tumor_filt$prior_malignancy %in% c(NA, \"'--\"),\n",
    "                                            which(is.na(KIRP_clin_data_tumor_filt$age_at_index))))\n",
    "KIRP_data_tumor_filt_priMalig <- KIRP_data_tumor_filt[,-KIRP_filt_race_age_gen_priMalig]\n",
    "KIRP_clin_data_tumor_filt_priMalig <- KIRP_clin_data_tumor_filt[-KIRP_filt_race_age_gen_priMalig,]\n",
    "KIRP_design_race_age_gen_priMalig <- model.matrix(~0 + prior_malignancy + race + age_at_index + gender, \n",
    "                                                  data=KIRP_clin_data_tumor_filt_priMalig)\n",
    "KIRP_voom_race_age_gen_priMalig <- voom(as.matrix(KIRP_data_tumor_filt_priMalig), KIRP_design_race_age_gen_priMalig, plot=FALSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af3b5e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>gene_id</th><th scope=col>symbol</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>3938</th><td>5002</td><td>SLC22A18</td></tr>\n",
       "\t<tr><th scope=row>5226</th><td>6580</td><td>SLC22A1 </td></tr>\n",
       "\t<tr><th scope=row>5227</th><td>6581</td><td>SLC22A3 </td></tr>\n",
       "\t<tr><th scope=row>5228</th><td>6582</td><td>SLC22A2 </td></tr>\n",
       "\t<tr><th scope=row>5229</th><td>6583</td><td>SLC22A4 </td></tr>\n",
       "\t<tr><th scope=row>5230</th><td>6584</td><td>SLC22A5 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & gene\\_id & symbol\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t3938 & 5002 & SLC22A18\\\\\n",
       "\t5226 & 6580 & SLC22A1 \\\\\n",
       "\t5227 & 6581 & SLC22A3 \\\\\n",
       "\t5228 & 6582 & SLC22A2 \\\\\n",
       "\t5229 & 6583 & SLC22A4 \\\\\n",
       "\t5230 & 6584 & SLC22A5 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | gene_id &lt;chr&gt; | symbol &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 3938 | 5002 | SLC22A18 |\n",
       "| 5226 | 6580 | SLC22A1  |\n",
       "| 5227 | 6581 | SLC22A3  |\n",
       "| 5228 | 6582 | SLC22A2  |\n",
       "| 5229 | 6583 | SLC22A4  |\n",
       "| 5230 | 6584 | SLC22A5  |\n",
       "\n"
      ],
      "text/plain": [
       "     gene_id symbol  \n",
       "3938 5002    SLC22A18\n",
       "5226 6580    SLC22A1 \n",
       "5227 6581    SLC22A3 \n",
       "5228 6582    SLC22A2 \n",
       "5229 6583    SLC22A4 \n",
       "5230 6584    SLC22A5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_SLC22_KIRP_nodes.df <- all_SLC22_nodes.df.filt <- SLC22_nodes_KIRP_exp <- all_SLC22_nodes.df[all_SLC22_nodes.df$gene_id %in% rownames(KIRP_voom_m$E),]\n",
    "\n",
    "all_SLC22_nodes_KIRP.df.filt <- all_SLC22_nodes.df[which(all_SLC22_nodes.df$symbol %in% c(\"SLC22A2\", \"SLC22A3\", \"SLC22A4\",\n",
    "                                                        \"SLC22A5\", \"SLC22A6\", \"SLC22A7\", \"SLC22A8\", \"SLC22A1\", \"SLC22A11\", \n",
    "                                                        \"SLC22A12\", \"SLC22A13\", \"SLC22A14\", \"SLC22A15\", \"SLC22A17\", \n",
    "                                                        \"SLC22A18\", \"SLC22A23\", \"SLC22A24\")),]\n",
    "head(all_SLC22_nodes_KIRP.df.filt)\n",
    "KIRP_data_tumor_filt_orig_m <- KIRP_data_tumor_filt_orig[,!KIRP_clin_data_tumor_filt$ajcc_pathologic_m %in% c(NA, \"'--\")]\n",
    "KIRP_voom_orig <- voom(as.matrix(KIRP_data_tumor_filt_orig_m), KIRP_design_m, plot=FALSE)\n",
    "\n",
    "SLC22_nodes_KIRP_exp_orig <- all_SLC22_nodes.df[all_SLC22_nodes.df$gene_id %in% rownames(KIRP_voom_orig$E),]\n",
    "SLC22_nodes_KIRP_exp <- all_SLC22_nodes.df[all_SLC22_nodes.df$gene_id %in% rownames(KIRP_voom_m$E),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89076680",
   "metadata": {},
   "outputs": [],
   "source": [
    "## with Race, Gender, & Age\n",
    "colnames(KIRP_design_all_m) <- gsub(\"ajcc_pathologic_m|^race|^gender\",\"\", colnames(KIRP_design_all_m))\n",
    "KIRP_contrasts_all_m <- makeContrasts(M1 - M0, levels=KIRP_design_all_m)\n",
    "colnames(KIRP_design_all_n) <- gsub(\"ajcc_pathologic_n|^race|^gender\",\"\", colnames(KIRP_design_all_n))\n",
    "KIRP_contrasts_all_n <- makeContrasts(N1 - N0, levels=KIRP_design_all_n)\n",
    "colnames(KIRP_design_all_stage) <- gsub(\"ajcc_pathologic_stage|^race|^gender\",\"\", colnames(KIRP_design_all_stage))\n",
    "KIRP_contrasts_all_stage <- makeContrasts(StageII - StageI, StageIII - StageII, \n",
    "                                           StageIV - StageIII, levels=KIRP_design_all_stage)\n",
    "colnames(KIRP_design_all_t) <- gsub(\"ajcc_pathologic_t|^race|^gender\",\"\", colnames(KIRP_design_all_t))\n",
    "KIRP_contrasts_all_t <- makeContrasts((T2 + T2a + T2b)/3 -  (T1 + T1a + T1b)/3, (T3 + T3a + T3b)/3 - (T2 + T2a + T2b)/3, \n",
    "                                       T4-(T3 + T3a + T3b)/3, levels=KIRP_design_all_t)\n",
    "\n",
    "KIRP_data_tumor_filt_race_age_gen <- KIRP_data_tumor_filt[,colnames(KIRP_data_tumor_filt)]\n",
    "KIRP_clin_data_tumor_filt_race_age_gen <- KIRP_clin_data_tumor_filt[rownames(KIRP_clin_data_tumor_filt) %in% \n",
    "                                                                    rownames(KIRP_design_race_age_gen),]\n",
    "KIRP_data_tumor_filt_race_age_gen <- KIRP_data_tumor_filt[,unlist(sapply(KIRP_clin_data_tumor_filt_race_age_gen$case_submitter_id, \n",
    "                                        function(x) grep(gsub(\"-\", \".\", x), colnames(KIRP_data_tumor_filt))))]\n",
    "KIRP_design_race_age_gen <- model.matrix(~0 + race + age_at_index + gender, \n",
    "                                   data=KIRP_clin_data_tumor_filt_race_age_gen)\n",
    "KIRP_voom_race_age_gen <- voom(as.matrix(KIRP_data_tumor_filt_race_age_gen), KIRP_design_race_age_gen, plot=FALSE)\n",
    "\n",
    "KIRP_race_age_gen_m_fit <- lmFit(KIRP_voom_all_m, KIRP_design_all_m)\n",
    "KIRP_race_age_gen_m_fit <- contrasts.fit(KIRP_race_age_gen_m_fit, contrasts=KIRP_contrasts_all_m)\n",
    "KIRP_race_age_gen_m_efit <- eBayes(KIRP_race_age_gen_m_fit)\n",
    "KIRP_race_age_gen_m_topTable <- topTable(KIRP_race_age_gen_m_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_m_topTable_SLC22 <- KIRP_race_age_gen_m_topTable[rownames(KIRP_race_age_gen_m_topTable) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_m_topTable_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_m_topTable_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_m_topTable_ADME <- KIRP_race_age_gen_m_topTable[rownames(KIRP_race_age_gen_m_topTable) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_m_topTable_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_m_topTable_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_n_fit <- lmFit(KIRP_voom_all_n, KIRP_design_all_n)\n",
    "KIRP_race_age_gen_n_fit <- contrasts.fit(KIRP_race_age_gen_n_fit, contrasts=KIRP_contrasts_all_n)\n",
    "KIRP_race_age_gen_n_efit <- eBayes(KIRP_race_age_gen_n_fit)\n",
    "KIRP_race_age_gen_n_topTable <- topTable(KIRP_race_age_gen_n_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_n_topTable_SLC22 <- KIRP_race_age_gen_n_topTable[rownames(KIRP_race_age_gen_n_topTable) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_n_topTable_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_n_topTable_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_n_topTable_ADME <- KIRP_race_age_gen_n_topTable[rownames(KIRP_race_age_gen_n_topTable) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_n_topTable_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_n_topTable_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_stage_fit <- lmFit(KIRP_voom_all_stage, KIRP_design_all_stage)\n",
    "KIRP_race_age_gen_stage_fit <- contrasts.fit(KIRP_race_age_gen_stage_fit, contrasts=KIRP_contrasts_all_stage)\n",
    "KIRP_race_age_gen_stage_efit <- eBayes(KIRP_race_age_gen_stage_fit)\n",
    "KIRP_race_age_gen_stage_topTable_1v2 <- topTable(KIRP_race_age_gen_stage_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_stage_topTable_1v2_SLC22 <- KIRP_race_age_gen_stage_topTable_1v2[rownames(KIRP_race_age_gen_stage_topTable_1v2) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_stage_topTable_1v2_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_stage_topTable_1v2_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_stage_topTable_1v2_ADME <- KIRP_race_age_gen_stage_topTable_1v2[rownames(KIRP_race_age_gen_stage_topTable_1v2) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_stage_topTable_1v2_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_stage_topTable_1v2_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_stage_topTable_2v3 <- topTable(KIRP_race_age_gen_stage_efit, coef=2, n=Inf)\n",
    "KIRP_race_age_gen_stage_topTable_2v3_SLC22 <- KIRP_race_age_gen_stage_topTable_2v3[rownames(KIRP_race_age_gen_stage_topTable_2v3) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_stage_topTable_2v3_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_stage_topTable_2v3_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_stage_topTable_2v3_ADME <- KIRP_race_age_gen_stage_topTable_2v3[rownames(KIRP_race_age_gen_stage_topTable_2v3) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_stage_topTable_2v3_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_stage_topTable_2v3_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_stage_topTable_3v4 <- topTable(KIRP_race_age_gen_stage_efit, coef=3, n=Inf)\n",
    "KIRP_race_age_gen_stage_topTable_3v4_SLC22 <- KIRP_race_age_gen_stage_topTable_3v4[rownames(KIRP_race_age_gen_stage_topTable_3v4) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_stage_topTable_3v4_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_stage_topTable_3v4_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_stage_topTable_3v4_ADME <- KIRP_race_age_gen_stage_topTable_3v4[rownames(KIRP_race_age_gen_stage_topTable_3v4) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_stage_topTable_3v4_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_stage_topTable_3v4_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_t_fit <- lmFit(KIRP_voom_all_t, KIRP_design_all_t)\n",
    "KIRP_race_age_gen_t_fit <- contrasts.fit(KIRP_race_age_gen_t_fit, contrasts=KIRP_contrasts_all_t)\n",
    "KIRP_race_age_gen_t_efit <- eBayes(KIRP_race_age_gen_t_fit)\n",
    "KIRP_race_age_gen_t_topTable_1v2 <- topTable(KIRP_race_age_gen_t_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_t_topTable_1v2_SLC22 <- KIRP_race_age_gen_t_topTable_1v2[rownames(KIRP_race_age_gen_t_topTable_1v2) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_t_topTable_1v2_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_t_topTable_1v2_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_t_topTable_1v2_ADME <- KIRP_race_age_gen_t_topTable_1v2[rownames(KIRP_race_age_gen_t_topTable_1v2) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_t_topTable_1v2_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_t_topTable_1v2_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_t_topTable_2v3 <- topTable(KIRP_race_age_gen_t_efit, coef=2, n=Inf)\n",
    "KIRP_race_age_gen_t_topTable_2v3_SLC22 <- KIRP_race_age_gen_t_topTable_2v3[rownames(KIRP_race_age_gen_t_topTable_2v3) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_t_topTable_2v3_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_t_topTable_2v3_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_t_topTable_2v3_ADME <- KIRP_race_age_gen_t_topTable_2v3[rownames(KIRP_race_age_gen_t_topTable_2v3) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_t_topTable_2v3_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_t_topTable_2v3_ADME), brin_ADME_nodes$EGID)]\n",
    "\n",
    "KIRP_race_age_gen_t_topTable_3v4 <- topTable(KIRP_race_age_gen_t_efit, coef=3, n=Inf)\n",
    "KIRP_race_age_gen_t_topTable_3v4_SLC22 <- KIRP_race_age_gen_t_topTable_3v4[rownames(KIRP_race_age_gen_t_topTable_3v4) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_t_topTable_3v4_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_t_topTable_3v4_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_t_topTable_3v4_ADME <- KIRP_race_age_gen_t_topTable_3v4[rownames(KIRP_race_age_gen_t_topTable_3v4) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_t_topTable_3v4_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_t_topTable_3v4_ADME), brin_ADME_nodes$EGID)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79c0c523",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(KIRP_design_race_age_gen_tx) <- gsub(\"treatment_type\",\"\",\n",
    "                                            gsub(\" \",\"\", gsub(\", NOS\",\"\", colnames(KIRP_design_race_age_gen_tx))))\n",
    "KIRP_contrasts_race_age_gen_tx <- makeContrasts(PharmaceuticalTherapy - RadiationTherapy, levels=KIRP_design_race_age_gen_tx)\n",
    "\n",
    "KIRP_race_age_gen_tx_fit <- lmFit(KIRP_voom_race_age_gen_tx, KIRP_design_race_age_gen_tx)\n",
    "KIRP_race_age_gen_tx_fit <- contrasts.fit(KIRP_race_age_gen_tx_fit, contrasts=KIRP_contrasts_race_age_gen_tx)\n",
    "KIRP_race_age_gen_tx_efit <- eBayes(KIRP_race_age_gen_tx_fit)\n",
    "KIRP_race_age_gen_tx_topTable <- topTable(KIRP_race_age_gen_tx_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_tx_topTable_SLC22 <- KIRP_race_age_gen_tx_topTable[rownames(KIRP_race_age_gen_tx_topTable) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_tx_topTable_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_tx_topTable_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_tx_topTable_ADME <- KIRP_race_age_gen_tx_topTable[rownames(KIRP_race_age_gen_tx_topTable) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_tx_topTable_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_tx_topTable_ADME), brin_ADME_nodes$EGID)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ffed3d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(KIRP_design_race_age_gen_priMalig) <- gsub(\"prior_malignancy\",\"\",\n",
    "                                            gsub(\" \",\"\",colnames(KIRP_design_race_age_gen_priMalig)))\n",
    "KIRP_contrasts_race_age_gen_priMalig <- makeContrasts(yes - no, levels=KIRP_design_race_age_gen_priMalig)\n",
    "\n",
    "KIRP_race_age_gen_priMalig_fit <- lmFit(KIRP_voom_race_age_gen_priMalig, KIRP_design_race_age_gen_priMalig)\n",
    "KIRP_race_age_gen_priMalig_fit <- contrasts.fit(KIRP_race_age_gen_priMalig_fit, contrasts=KIRP_contrasts_race_age_gen_priMalig)\n",
    "KIRP_race_age_gen_priMalig_efit <- eBayes(KIRP_race_age_gen_priMalig_fit)\n",
    "KIRP_race_age_gen_priMalig_topTable <- topTable(KIRP_race_age_gen_priMalig_efit, coef=1, n=Inf)\n",
    "KIRP_race_age_gen_priMalig_topTable_SLC22 <- KIRP_race_age_gen_priMalig_topTable[rownames(KIRP_race_age_gen_priMalig_topTable) %in% as.character(all_SLC22_KIRP_nodes.df$gene_id),]\n",
    "KIRP_race_age_gen_priMalig_topTable_SLC22$Symbol <- all_SLC22_KIRP_nodes.df$symbol[match(rownames(KIRP_race_age_gen_priMalig_topTable_SLC22), all_SLC22_KIRP_nodes.df$gene_id)]\n",
    "KIRP_race_age_gen_priMalig_topTable_ADME <- KIRP_race_age_gen_priMalig_topTable[rownames(KIRP_race_age_gen_priMalig_topTable) %in% as.character(brin_ADME_nodes$EGID),]\n",
    "KIRP_race_age_gen_priMalig_topTable_ADME$Symbol <- brin_ADME_nodes$Sym[match(rownames(KIRP_race_age_gen_priMalig_topTable_ADME), brin_ADME_nodes$EGID)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04f80f19",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "11331   644 "
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "   10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "  404    26 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_n\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       " 9816  2159 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "    5     5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "  323   107 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_stage - 1v2\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "11975 "
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "   10 "
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     "output_type": "display_data"
    },
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      "text/plain": [
       "\n",
       "FALSE \n",
       "  430 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_stage - 2v3\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "11952    23 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
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       "   10 "
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    {
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       "\n",
       "FALSE  TRUE \n",
       "  429     1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_stage - 3v4\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "11958    17 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "   10 "
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       "\n",
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       "  430 "
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_t - 1v2\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "11975 "
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       "\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_t - 2v3\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "11953    22 "
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     },
     "metadata": {},
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       "\n",
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       "   10 "
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     "metadata": {},
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    {
     "data": {
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       "\n",
       "FALSE  TRUE \n",
       "  429     1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_t - 3v4\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "11975 "
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     },
     "metadata": {},
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       "\n",
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       "\n",
       "FALSE \n",
       "  430 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_priMalig\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "11975 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "   10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "  430 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DE Genes - ajcc_pathologic_tx\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "11975 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "   10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE \n",
       "  430 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "message(\"DE Genes - ajcc_pathologic_m\")\n",
    "table(KIRP_race_age_gen_m_topTable$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_m_topTable_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_m_topTable_ADME$adj.P.Val < 0.1)\n",
    "message(\"DE Genes - ajcc_pathologic_n\")\n",
    "table(KIRP_race_age_gen_n_topTable$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_n_topTable_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_n_topTable_ADME$adj.P.Val < 0.1)\n",
    "\n",
    "message(\"DE Genes - ajcc_pathologic_stage - 1v2\")\n",
    "table(KIRP_race_age_gen_stage_topTable_1v2$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_1v2_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_1v2_ADME$adj.P.Val < 0.1)\n",
    "message(\"DE Genes - ajcc_pathologic_stage - 2v3\")\n",
    "table(KIRP_race_age_gen_stage_topTable_2v3$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_2v3_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_2v3_ADME$adj.P.Val < 0.1)\n",
    "message(\"DE Genes - ajcc_pathologic_stage - 3v4\")\n",
    "table(KIRP_race_age_gen_stage_topTable_3v4$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_3v4_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_stage_topTable_3v4_ADME$adj.P.Val < 0.1)\n",
    "\n",
    "message(\"DE Genes - ajcc_pathologic_t - 1v2\")\n",
    "table(KIRP_race_age_gen_t_topTable_1v2$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_1v2_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_1v2_ADME$adj.P.Val < 0.1)\n",
    "message(\"DE Genes - ajcc_pathologic_t - 2v3\")\n",
    "table(KIRP_race_age_gen_t_topTable_2v3$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_2v3_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_2v3_ADME$adj.P.Val < 0.1)\n",
    "message(\"DE Genes - ajcc_pathologic_t - 3v4\")\n",
    "table(KIRP_race_age_gen_t_topTable_3v4$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_3v4_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_t_topTable_3v4_ADME$adj.P.Val < 0.1)\n",
    "\n",
    "message(\"DE Genes - ajcc_pathologic_priMalig\")\n",
    "table(KIRP_race_age_gen_priMalig_topTable$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_priMalig_topTable_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_priMalig_topTable_ADME$adj.P.Val < 0.1)\n",
    "\n",
    "message(\"DE Genes - ajcc_pathologic_tx\")\n",
    "table(KIRP_race_age_gen_tx_topTable$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_tx_topTable_SLC22$adj.P.Val < 0.1)\n",
    "table(KIRP_race_age_gen_tx_topTable_ADME$adj.P.Val < 0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b403395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'6582'</li><li>'5002'</li><li>'6581'</li><li>'55867'</li><li>'6584'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '6582'\n",
       "\\item '5002'\n",
       "\\item '6581'\n",
       "\\item '55867'\n",
       "\\item '6584'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '6582'\n",
       "2. '5002'\n",
       "3. '6581'\n",
       "4. '55867'\n",
       "5. '6584'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"6582\"  \"5002\"  \"6581\"  \"55867\" \"6584\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M1mAwCFQhF/aSOqsCxrtVoBQCaTxdmXw/379x89elTsUnRTcfWXFJzf/va3ffr0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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp.KIRP_race_age_gen_n <- which(KIRP_clin_data_tumor_filt_all_n$ajcc_pathologic_n %in% c(\"N0\", \"N1\"))\n",
    "tmp.KIRP_race_age_gen_n_sig <- rownames(KIRP_race_age_gen_n_topTable_SLC22)[KIRP_race_age_gen_n_topTable_SLC22$adj.P.Val < 0.1] \n",
    "tmp.KIRP_race_age_gen_n_sig\n",
    "KIRP_race_age_gen_n_inp <- do.call(rbind.data.frame, lapply(tmp.KIRP_race_age_gen_n_sig, function(x)  {\n",
    "        cbind.data.frame(KIRP_clin_data_tumor_filt_all_n[tmp.KIRP_race_age_gen_n,],\n",
    "                         Gene=rep(KIRP_race_age_gen_n_topTable_SLC22$Symbol[rownames(KIRP_race_age_gen_n_topTable_SLC22) %in% x], \n",
    "                              nrow(KIRP_clin_data_tumor_filt_all_n[tmp.KIRP_race_age_gen_n,])),\n",
    "                         Value=as.numeric(KIRP_voom_all_n$E[rownames(KIRP_voom_all_n) %in% x, tmp.KIRP_race_age_gen_n]))\n",
    "    }))\n",
    "KIRP_race_age_gen_n_inp$Gene <- factor(as.character(KIRP_race_age_gen_n_inp$Gene), \n",
    "                               levels=c(\"SLC22A2\",\"SLC22A18\", \"SLC22A3\", \"SLC22A11\", \"SLC22A5\"))                                                            \n",
    "\n",
    "ggplot(KIRP_race_age_gen_n_inp, aes(x=Gene, y=Value, fill=ajcc_pathologic_n)) + geom_violin() +\n",
    "    theme_bw(base_size=16) + ylab(\"Log2(CPM)\") + labs(title=\"KIRC - Lymph Node Progression\") + xlab(\"\") +\n",
    "    theme(legend.title=element_text(size=0)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n",
    "    stat_summary(fun = \"median\", geom='point', inherit.aes=TRUE, position=position_dodge(0.95)) + scale_color_brewer(palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50da8ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IEerg5CgU4ekEvISB8vpVK5a9cunTwgAABAQ1DsAP5fVVXVnj17zHncj9t66PBh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FQw89RNJG2kWY1GBXXl5ORHPmzDn2oSlT\npnz33Xf5+fk2m+3888//6KOPQlkgAAD0KCgooJ6xazIn9sOKBQPIiFYr9eCgfvvb35aUlHz/\n/fcff/xxCKoKHanBzmazEVFOTs6AjxYUFHzzzTdjxoxxOp2XXXbZv//975AVCAAAPcScpI2G\nYXa65ibClFiQoemnSz04KI7jFi5cSEQLFiwQ5LRsuNRgl5CQQEStra3HOyEnJ2fjxo3jxo1z\nu91XXXXV2rVrQ1MgAAD0ONpiFw1dsdrGRiIS59gByMgll9GUqb84UnoCXXV1MFe65JJJkybt\n2LHj3XffDU1toSA12BUXFxPRtm3bBjknIyPj66+/Likp8Xq9N9xww8svvxyCAgEAoEdaWprR\naNRGQ1estgldsSBLPE+PLaUHH6YLL6bzL6S/zaenlpNSFdzFHn74YSJ68MEHfT5fSKsMntRg\nd/rppxPR+++/P/hpaWlpX3311UknneTz+VavXj3U6gAA4JcKCgo07e3ynxira2pKTk42mUys\nCwE4BsfRWWfTXf9L/3MvzfoVDWHLu9mzZ0+bNm3v3r1vvvlmCAscCqn/GHEDif3792/cuHHw\nM5OTkzds2HDqqacOtTQAADhGXl4eCT5NSzPrQgajcNhVXWb0w0IsWLx4MRE999xzrAs5Smqw\nmzJlykknnZScnPzss8/6PTk+Pv7zzz8/66yzhlQaAAAcIy8vj2Q/zE7b1EQYYAexYcaMGTNm\nzBDnmPbz2WefTe1hNpuJ6Oqrrxb/84knnghTPUrppwa0t4TRaPzyyy8H/HcCAEDQxLSkbZZ1\ni5240p6YQQGGvcWLF0+fPv3Y4y0tLT/88EPfI7t27RJ/OOGEE8JUTADBLlA8zxuNxvBdHwAg\nBolpSeZrFIvlocUOhpnG48xbmjZt2oArnlxzzTXXXHNNmIvqL/gBgwAxTlYLF0HsyM7OVqvV\ncg92Lc1ElJuby7oQgJiDYAcAEE14ns/JydG0tJBsllc4lralRa/Xp6WlsS4EIOYE2RX7wAMP\nSLq6UmkymXJyciZNmoTVjAAAQiIvL6+iokJt7nQlJrGuZQCcz6dpbR05upDjONa1AMScIIPd\nI488EuhTJk+e/Oijj86cOTO4OwIAgEjs4tS2tMgz2Knb2jivZ+TIkawLgdgVV3Fo/NLHQnIp\nldkckutETJDBLjs7m4gsFktXV5d4xGg0GgwGq9Xa3d0tHjGZTHFxcd3d3eIU3y1btsyaNev5\n55//4x//GIrKAQBilJiZtC3NXWOKWNcyAG1rM2FKLDDC87xerycisnSF6Ioc6fX8EBYxjrAg\ng11tbe0nn3xy9dVXZ2dn33///RdeeKEY9Yiorq7ugw8+eOSRR+x2+6pVq2bPnt3a2rpu3boF\nCxbU1NT86U9/mjFjRlGRHH8ZAQBEBbHFTtPSwrqQgWlbWqgnfQJEmMlk+uabb1hXwVKQCXTf\nvn2XX355Wlrajz/+OG/evN5UR0TZ2dm33nrrTz/9lJaWdvnll+/bty8lJeX666/fvn17bm6u\n1+tdvnx5iIoHAIhFR1vsWmW6lJ2mFcEOgJkgg90TTzxhs9mWLl2ampo64AmpqalLly4Vz+k9\n8tBDDxHR119/HdxNAQCAiBISEkwmk6a1lXUhAxPXOkGwA2AiyGC3YcMGIjr99NMHOUd89Msv\nv+w9MmvWLCKqra0N7qYAACAaMWKEur2N83pZFzIATUtLUlISFqgHYCLIYNfU1ET+FmgVH23u\ns++NuKaR0+kM7qYAACAaOXIk5/NpOtpZF9If7/GozeYRI0awLgQgRgUZ7BITE4noq6++GuQc\nsVUvKenn2fjiFNrj9d4CAIBEYnISR7PJirqtlQRfTk4O60IAYlSQwW7GjBlEdM8999TX1w94\nQn19/b333tt7pmjbtm1ElJWVFdxNAQBAJCYnGQY7ceQfWuwAWAky2N13331KpbKioqK0tPTx\nxx/fv3+/1+slIq/Xu3///scff7y0tLSiokKpVP7lL3/pfdZbb71FRNOnTw9J6QAAMasn2LWx\nLqQ/bVsr9ZQHAJEX5Dp2paWlr7zyyg033NDa2nrffffdd999HMdpNBqn09k78E6pVK5Zs2bi\nxInif5rN5qqqqunTp1988cWhqR0AIFYdDXZtspsYK5aEFjsAVoJfSfnaa6/94YcfentaBUFw\nOBxiquM47uyzz96yZctVV13Ve358fPyGDRs2bdp0xhlnDLFoADkYfPIQQFglJSXp9XrZBru+\ni5sCQCQF2WInOvnkkzds2HDkyJHNmzdXVVV1d3cbjca8vLxp06ZhBSOIEYh3wEp2dra1spJ8\nPpLTZkfq1laj0ZiQkMC6EIAYNaRgJxo5ciRiHABAhGVnZ5eXl6u7ulyySVGcIGg6OrJGFbAu\nBCB2yejvPAAAkE7s7lS3y6g3VmXu5LwezJwAYGioLXZtbW2vv/76pk2bqqqqLBZLXFxcXl7e\n6aeffvXVV/ddwQ4AAEJLDHaatrbugkLWtRylaW8nrGkFwFTwwU4QhKeffvr++++32+19j2/d\nuvXtt9++7777Hnvssdtvv33IFQLIFEbXAVtHg11HB+tCfqbGzAkA1oIPdn/729+WLFki/pyS\nklJcXBwXF9fd3b13797W1labzXbHHXe0tLQsWrQoRKUCAMDPMjMzqSdLyQRa7ACYC3KM3Q8/\n/CCmuokTJ65fv765uXnjxo3r1q37+uuvm5ubN2zYcMIJJxDRI488snXr1lDWCwAARESUlZXF\ncZy8Wuza26gncQIAE0EGuxUrVhDRiSee+N13351zzjkcx/U+xHHcjBkzNm3adPLJJwuCIJ4J\nMPygKxbY0mq1iYmJYpaSCU1HB8dxaLEDYCjIYPfNN98Q0SOPPGI0Ggc8wWAwPPbYY71nAgxX\nPp+PdQkQuzIzM1XmTs7rZV3IUer2tsTERK1Wy7oQgNgVZLBramoiokmTJg1yjvhoQ0NDcLcA\nkDmxxQ7tdsBQVlYWJwhqs5l1IUREnM+nMneiHxaArSCDnVqtJqJ+82H7sdlsRKTRaIK7BYDM\nIdIBc0fnT3S0sy6EiEjV2cEJAoIdAFtBBrv8/HwiWrdu3SDniI8WFGAJchie0GIHzGVkZJBs\ngp2ms5MwcwKAtSCD3Zw5c4ho/vz5O3fuHPCEXbt23X///b1nAgBAyPUEO1lMjBXLEEsCAFaC\nDHZ33nlnfHx8e3v71KlT77777s2bN5vNZq/XazabN2/efPfdd0+ZMqWtrS0hIeHOO+8MbcUA\nMiG21WHyBDB0NNh1yiPYdbYTgh0Aa0EuUJyWlvbOO+9ceOGFNptt2bJly5YtO/Ycg8Hw3nvv\npaSkDK1CAJnyer2ErlhgqifYdbIuhIhIhRY7ABkIssWOiGbOnLl9+/bZs2f3XcROxHHcnDlz\nduzYcdZZZw2pOgDZQ7ADhkwmk16vl02LXScRpaensy4EIKYFv6UYEY0dO/b//u//amtrN23a\nVF1dbbFY4uLi8vLyTjvtNOwVCMOe2Anrlc0SYhCb0tPTLfX1rKsgIlKbO7VabUJCAutCAGLa\nkIKdKCcn58orrzz2+IEDB8QBdp9++unQ7wIgN2KwQ4sdsJWenl5ZWamw2706HdtK1J0daWlp\nbGsAgBAEu+Pp7Oz87LPPwnd9ALbQYgdyIGYptbnTzjTY8U6nwm5HPywAc8GPsQOIcWKww6xY\nYEucrKBiPX9C3WWmnpQJAAwh2AEEyePxEFrsgLXU1FQiUpsZBztVZwdh5gSADCDYAQRJjHRi\nvANgRWwkU7EOduJ+tWLKBACGEOwAgiQGO7TYAVtHW+y6utiWoTKjKxZAFhDsAILkdrsJLXbA\nWk+LnZltGeIYO7TYATCHYAcQJDHSifEOgJX4+Hi1Wq3qYhzsVAh2APKAYAcQJLTYgRxwHJeS\nksK8xU7VaeZ5Pjk5mW0ZACB1Hbuvv/460Evv27cv0KcARBEx2LlcLtaFQKxLSUmpb2jkvF5B\noWBVg8rSlZSUxPNoLABgTGqwmzFjRljrAIg6YqRDsAPmUlJSSPCpui2ueEbbeQmCytKVMno0\nm7sDQB/46wogSE6nkxDsQAZSUlKISMVuYqzSauW8XgywA5ADqS12r776aljrAIg6YrBzOBys\nC4FYdzTYWZgFO/HWGGAHIAdSg931118fzjIix+v1YrQ7hITdbhf/F++o8Ondsc3j8QiCwLYY\n2UpMTCQiJbsWOzHYJSYm4rMAIp/Phw8sK1KD3TAgfkN0dXV1st5UEYYHs9lMRDabDe+oCLBY\nLKxLkC+NRkM9C44wobJYiEin0+GzACKHw4HF21mJoWAnTtdKSEhAfwGEhPhry+Fw4B0VPna7\n3WazEVFCQoKC3ZRPmcvLyyMiVXc3qwLEFrvc3Fx8FkCk1+uVyhgKGLISc687x3Ecx7GuAoaD\n7u5uInI4HD6fD5kjTHo/rfjkDqJnjB2zRk1ll4WIkpOT8f8RAHOYFQsQJDHYCYJgtVpZ1wIx\nLTExkeM4JcPJE9ZuIkpKSmJVAAD0QrADCFLvqK8u1vuvQ4xTqVRxcXFiumJCzJQIdgBygGAH\nEAyfz9fbUIdgB8wlJSUpLezG2HV3q9Vqo9HIqgAA6IVgBxAMs9ncuxIHZgICc4mJiQqHnWO0\n2oiy2yIuuQIAzCHYAQSjo6ODiMSB4gh2wJyYq9hMjBUEpdWKflgAmUCwAwiGGOwytJrenwEY\nEnOVksUwO6XDznm9aLEDkAkEO4BgtLW1EdGoOEPvzwAMJSQkEJGym8GKJ8rubuppMgQA5hDs\nAILR2tpKRGOMht6fARg6uqsYi5V3xJuKyRIAmEOwAwhGS0sLEY2PNxJRc3Mz63Ig1h0NdizG\n2KHFDkBWEOwAgtHU1EREuQa9SakUfwZgKD4+noiUNhYtdnhXjkgAACAASURBVDZrbwEAwByC\nHUAwGhoaeI5L1ajTtZqmpqbepU8AmEBXLACIEOwAglFfX5+iVik5LlOncblcGGYHbB1tsWMS\n7NBiByAnCHYAAbPb7e3t7Vl6LRFlajVEVFtby7ooiGk9XbG2yN9a0d1NaLEDkA0EO4CA1dbW\nCoKQo9MS0Qi9jhDsgDWtVqvRaNiMsUNXLICcINgBBOzIkSPUE+ly9LreIwAMxcfHK5h0xdpt\nHMeZTKbI3xoAjoVgBxCwqqoqIhqh1xLRSL2WiKqrq9mWBGAymZh0xSptNp1Op1QqI39rADgW\ngh1AwCorK4koT68nojStRq9UiEcAGDKZTLzbxXs8Eb6vwmZFPyyAfCDYAQSsoqJCzfM5ei0R\ncUR5el1NTY3L5WJdF8Q0MV0pIj7MTmm3x8XFRfimAHA8CHYAgfF6vVVVVbkGnYLjxCP5Rr14\nkGldEOvEdKW02yN5U97l4jwerHUCIB8IdgCBqa6udrlc+QZ975FRRgMRHTp0iF1RACROX1BE\ndpid0m6jnkwJAHKAYAcQmIMHDxJRofHnYFdo0BNRWVkZs5oAetJVhIOdeDtMiQWQDwQ7gMCI\nAa4oztB7ZHScgUOwA9bEdKV0RLQrVoEWOwCZQbADCExZWRlHNMZk7D1iUikztJqysjJBEBgW\nBjGOTVesw0EIdgBygmAHEABBEPbv35+h05p+uWrXWJPRYrFg/wlgyGg0Uk8TWsSgKxZAbhDs\nAAJQU1NjsVjG9umHFRXFGYlo//79LIoCIOqdFetwRPKmCrudejIlAMgBgh1AAPbt20dEY039\nv8bGxRuJaO/evQxqAiCi3skTkV3uBGPsAOQGwQ4gAGJ0Kz4m2BXFGTgEO2CqJ9hFtivW4SC0\n2AHICYIdQAD27t3Lc1zRMcEuTqkcadAdOHDA6/UyKQzgaLCLbFcsJk8AyA2CHYBUHo+nrKws\nV68zKBTHPjouzuhwOA4fPhz5wgCISK1Wq1SqiHfFYowdgLwg2AFIVV5e7nQ6i+MH/g4T+2f3\n7NkT2aIAfhYXFxfhFjuFw05osQOQEwQ7AKnE0FZsGvg7rDg+jhDsgCmj0Rj5FjulUqnVaiN5\nUwAYBIIdgFTHmzkhKjTq1TyH+RPAkNFoVDgj3GLnQD8sgKwg2AFItXfvXg3PF/TZJbYvFc+P\njjNWVlbaIrv0P0Avo9HIeb282xWxOyLYAcgNgh2AJFartbq6enScQclxxzun2GT0+XwHDhyI\nZGEAvXo2n4hcb6zCYTcY+q/XDQAMIdgBSHLgwAGfz3e8fljRuDgj9SxiDBB5R4NdpOZPcB4P\n5/GgxQ5AVhDsACQ53p4TfRWZDIRgB+yIjWcRC3bilFi02AHICoIdgCRiB+vYuMGC3Ui9Tq9Q\noCsWWIlwix22nQCQIQQ7AEkOHDigVypG6Adb1oHnuNFxhpqaGqvVGrHCAHodbbGL1MRYBDsA\nGUKwA/DPZrPV1NSMMuj548+cEI02GgRBOHjwYGQKA+iLSVcsgh2ArCDYAfh36NAhn883Os7/\nWKIxJgMRlZeXh78ogP7EYMdHalaswuEkjLEDkBkEOwD/Dh06RESFRv9fYAUGfe/5ABHWM8Yu\nUsHOia5YANlBsAPw7/Dhw0R0vKWJ+8o36HmOQ7ADJnqCnTMytxP7fNFiByArCHYA/lVWVhJR\nrl7n90ytgs/QasTzASLsaLCL3OQJLHcCIDsIdgD+VVVVJalVJpVSysl5Bp3FYmlrawt3VQD9\n6PV6iuDkCd6OFjsA2UGwA/DDZrO1tLSMlNBcJxLPrK6uDmdRAAOIdIsdxtgByA+CHYAftbW1\ngiCMkBzscnRaIqqpqQlnUQAD0Ov1HMdhuROAWIZgB+BHbW0tEWXpNBLPz9Zriaiuri6MNQEM\nhOd5vV6viNhyJ3a02AHIDoIdgB8NDQ1ElKUbbM+JvjK1GiKqr68PY00Ax2E0GvmItdg5McYO\nQHYQ7AD8EINdhlZqi126VsMRNTY2hrMogIEZDIbIrWPncOh0OoVCEZnbAYAUCHYAfjQ3NxNR\nuuRgp+b5BLWqqakpnEUBDMxoNCpcLk4QInAvhcOBflgAuUGwA/CjpaWF57hEtUr6U1LU6tbW\nViEiX64AfRmNRhKEyOwqprDbEewA5AbBDsCPtra2eJVSyXHSn5KsUbnd7q6urvBVBTCgyO0q\n5vMpnE4EOwC5QbAD8KO9vT1RFUBzHRElqtXiE8NTEcBx9QS7sM+fULicJPgQ7ADkBsEOYDAO\nh8PhcCRqAg12KiIym83hKQrguCLWYidmRwQ7ALlBsAMYjNidalJK2kysV5xSQQh2wEJcXBwR\nKSPQYme3994OAOQDwQ5gMGKwMwYY7EwqVe9zASJJTFoRWKNYYbcRWuwA5AfBDmAwVquViIzK\nwFbqMigVRGSz2cJSE8DxHe2KtYf9vSc2CqLFDkBuEOwABiOGM32AwU7H84RgByyYTCaKUIsd\numIB5AjBDmAwDoeDiDR8YJ8UnUJBRPZIbdkJ0KunxS5CXbEIdgByg2AHMBgx2GkDDHZaBd/7\nXIBIElvslBEIdjYEOwA5QrADGIzL5SIiVYDBTsnzvc8FiKSjXbHhHwYgZkfxdgAgHwh2AINx\nu91EpOID2HaCiMRtKrxeb1hqAjg+sSs2Yi12CHYAcoNgBzAYn89HRDwFE+w8Hk9YagI4PrVa\nrdPpIjIrFpMnAOQIwQ5gMEeDXYAtdjxa7IAdk8mkDH9XrMJq5TgOLXYAcoNgBzAYQRCIAmyv\nIyISQl8KgDQmk0lhs4b7Lkq7Xa/XKxSBrQQEAOGGYAcwGI7jKJiYxvU+FyDC4uPjebebd7vD\neheF1RofHx/WWwBAEBDsAAbD8zwR+YTAop1X8PU+FyDCjq54EtZGO0FQ2m0IdgAyhC8egMGI\nPU2BB7ufnwsQYQkJCUSksIYx2CmcDs7rRbADkCEEO4DBKJVKInL7Agt2HkEgIpVKFZaaAAYl\nBjultTt8txAnZyDYAcgQgh3AYMRw5g6wxc7t8xGCHTAi5q2wTowVU6OYIAFAVhDsAAaj1WqJ\nyBngwiUun6/3uQAR1tNiF8auWPHiCHYAMoRgBzAYMZw5fL6AnuXwItgBM5HoikWwA5ArJesC\npPJ6vR9++OGGDRsaGho0Gs24ceN++9vfjh49mnVdMMzpdDoicgTYYmfzegnBDhhJTEwkImV3\nOINdd3fvjQBAVqIj2Hm93kWLFu3YsUOn002YMKGrq2vLli3bt2//29/+dsopp7CuDoYzg8FA\nRFZPYMHO7vX2PhcgwsSGNFVYu2K7LYRgByBL0RHsPvroox07duTm5i5evFgcF/ztt98uXbp0\n2bJlq1atwtcnhI/47rIF2GInBkG8M4GJnhY7S/huIaZGBDsAGYqCMXaCILz33ntENG/evN7Z\n9aeffvq0adO6u7s/++wzptXBMCfucW5xBxbsuj0ewv7owIhOp9PpdGHuikWLHYBMRUGwKy8v\n7+joSElJKS4u7nv89NNPJ6Lvv/+eUV0QE4xGI8/zXR5PQM/q8ngJwQ7YSUxMVIWzxU5psSgU\nCqxjByBDURDsKisriWjUqFH9joszJ6qqqoQA1xgDkI7neaPR2OUOLNiZXW7CnEFgJzk5WWm1\ncQHO5pZOZbEkJCRg0zwAGYqCMXbNzc1ElJKS0u94cnIyETkcDovFIu6N2JfNZvP8spXFarUS\nkSAICIIQkISEhM6G+oCe0ulyE5HJZMKbbYh6X0B8cgOSlJREgk/Z3e0+5ndjSKi6LckFBfh/\nBECGoiDY2e12GmjlCIVCoVKp3G633W4/NtjdcccdP/74Y98jaWlpRNTZ2dnW1hbOemG4MRqN\nNW6PRxCUHCfxKWa3R6VSORwOh8MR1tpiR2dnJ+sSook4cUdl6QpHsFPY7ZzHExcXh9+lcDzH\ntq1AxERNQzo30Hcq/l6ECEhISBB6GuEkane70Q8LDB2dGGsJyzA7laWLMHMCQK6ioMVOXCFW\nbLfry+v1in8QiCf0c+mll5522ml9j3g8npUrV+r1eixCAQFJTU0lojaXO0WjlnK+j6jT5R6d\nnIx32tC53W6Xy0VEOp0OI7qky8jIICJVlzkcF1dZLESUlpaGdzgcj1qtxgeWlSgIduLXamtr\na7/jYi+AVqsdcO7h7Nmz+x1paGhYuXKlRqMZMAgCHE96ejoRtbtcRJK+xjpdbq8gpKWl4Z0W\nEmKw02q1CoWCdS1RIzMzk3qa1kJO1dVFRBkZGXiHw/EolUoEO1ai4HUvKCggosOHD/c7Xl5e\nTkR5eXkD9tIChIo4cafNKbUrts3looGm+wBEjPj3sCpcXbHm3lsAgNxEQbAbPXp0YmJia2vr\nvn37+h7/9ttviWjq1KmM6oJYIU67aXW5JJ7f6nQRvvaAKfHvCpU5PF2xZjPhTxcAuYqCYMdx\n3EUXXUREK1euNPf8nvr22283b95sMBjOPfdcptXB8CdGtBaHU+L5CHbAXHJyMs/z6nB2xeId\nDiBPUTDGjoguuuiinTt3/vjjj3/4wx/GjRtnNpsPHTrE8/xdd91lNBpZVwfD3NFRnpJnxbY4\nXdTTzgfAhFKpTExMdISpxa4LXbEA8hUdwU6hUCxYsOCDDz7YsGHD7t271Wr15MmTr7jiijFj\nxrAuDYa/xMRElUrVLLnFrtmBFjtgLy0trfVgOfl8FOox7Oous8lk0mg0ob0sAIREdAQ7IlIo\nFJdeeumll17KuhCIORzHpaamNre2SDy/xemknrm0AKykpqZy+/erbFa3McR7Fqs6O9Nyc0N7\nTQAIlSgYYwfAXHp6utnldknbebPF6dJoNMfuhgIQSeJgAFWod+xQ2qy8242RBgCyhWAH4F9q\naqpA1CZtmF2Lw4XmOmBOzF5qc4iDnZgU8Q4HkC0EOwD/xO9IKRNjHV5fl8eDAXbAnJi91KFu\nsROTIlrsAGQLwQ7AP3HJrman/6XsxLVOsMQXMHd0V7GOjtBeVo0WOwB5Q7AD8K9n8wn/wU7c\ndgItdsDc0RY7c1iCnZgaAUCGEOwA/Dsa7CRsPoEWO5CJtLQ0juPUoW+x6yAEOwAZQ7AD8E/6\ndrHtLjcRJScnh70mgEGp1eqkpCQxh4Xysh0dHMdhjB2AbCHYAfiXlJRERJ1uj98zO1zu3vMB\n2MrMzFSZzZy0ZXokUne0JyYmarXaEF4TAEIIwQ7Av7i4OIVCYXb7b7HrdLuJKDExMfxFAfiR\nkZHBCYIqdI12nM+nMneiHxZAzhDsAPzjOC4+Pr5Twjp2XW4PEcXHx4e/KAA/MjMziUgTuhVP\nVF1mzudDsAOQMwQ7AElMJlO3x+v3NIvHK54c/ooA/BCDnbq9LVQXFC+VlZUVqgsCQMgh2AFI\nEhcXZ/V6fYIw+GndHo9KpdLpdJGpCmAQIQ92mvZ2QrADkDcEOwBJjEajTxDs/sahWz1eg8EQ\nmZIABpednU1EmtCteIIWOwD5U7IuACA6iI1wDo/XoFAMcprd60VzHciE2GKXsHvXuIb6kFxQ\nXJ0YwQ5AzhDsACTRaDREZPf6abFzeL1xWAkC5EGn002cOHHXrl362ppQXTMrKysnJydUVwOA\nkEOwA5BErVYTkdvfGDuXT1CpVBGpCMC/V155hXUJABBRGGMHIIkY19z+xth5BAQ7AABgBsEO\nQBKFQkFE3kFb7HyC4BMExaCD8AAAAMIHwQ5AEo7jiEigwYKd0OdMAACAyEOwA5BEEAQi4miw\n0IZIBwAAbCHYAUjSE+wGw/U5EwAAIPIQ7AAk8Xq9RKTgB22xI+KJPB5PpIoCAAD4BQQ7AEnE\nuKbk/HxklDyPYAcAAKwg2AFI4na7iUjlbxSdiuPEMwEAACIPwQ5AEqfTSUQahZ+PjFrBi2cC\nAABEHoIdgCQul4uINLyfj4xWoRDPBAAAiDwEOwBJHA4HSWix0/BosQMAAGYQ7AAkcTqdHJHa\nf4sdL0ZAAACAyEOwA5DE4XBoBl/shIiI1Dzvdrt9/raUBQAACAcEOwBJHA6Hhve/CaxOwROR\n3W4Pf0UAAAD9IdgBSGK323X+BtgRkZZXUM+APAAAgAhDsAOQxOl0+p05QT2zKxDsAACACQQ7\nAEkcDofa37YTRKTmOepZ9A4AACDCEOwA/BMEwe12q/xNiSUijUJBCHYAAMAIgh2Afx6PRxAE\nlf9JsaQkop79xwAAACIMwQ7AP4/HQ0RSWuyUPE9EXq837DUBAAAcA8EOwD8xqEn5tIiNegh2\nAADABIIdgH+CIBARz/nviuWJIyIsUAwAAEwg2AH4x3EcEQkkBHQ+AABAhCHYAfinUCiIyCsh\n13kFofd8AACACEOwA/BPqVQSkUfw38Hq9vl6zwcAAIgwBDsA/1QqFc/zLq//YOcSBCLSaDTh\nLwoAAKA/BDsASbRarUNCsBPP0el04a8IAACgPwQ7AEkMBoNNwiImdq+XEOwAAIARBDsASeLi\n4ixuj9/Tutwe8eTwVwQAANAfgh2AJCaTyeb1egQ/M2O73B6NRqPVaiNTFQAAQF8IdgCSJCQk\nCESdLj+bwHa43QkJCZEpCQAAoB8EOwBJkpOTiaht0GDnI+p0ucUzAQAAIg/BDkCStLQ0Impx\nOAc5p93p8gqCeCYAAEDkIdgBSJKenk5ETYMGO/FR8UwAAIDIQ7ADkCQzM5OIGp2uQc5pcDh7\nzwQAAIg8BDsASbKysoio3mYf5Jx6u6P3TAAAgMhDsAOQJD09Xa1W1w3aFVtrcxDRiBEjIlUU\nAADALyDYAUjC83xOTk6Nze47/lJ2R2x2nucR7AAAgBUEOwCp8vLyHF5f8/GH2R2xOdLT07E6\nMQAAsIJgByBVXl4eEVVZBx5m1+Fym93u/Pz8iNYEAADQB4IdgFQFBQVEVGm1DfioGPgQ7AAA\ngCEEOwCpRo0aRccPdhVWW+85AAAATCDYAUiVm5urVCoPdw8c7A53W4mosLAwskUBAAD8DMEO\nQCq1Wp2bm1vZbfMN9OjhbhvP82J3LQAAABMIdgABKCwsdPp8NcfMnxCIKrptOTk5mBILAAAM\nIdgBBGD06NFEdKjb2u94g91h83rFRwEAAFhBsAMIgBjdKo4ZZlfebe19FAAAgBUEO4AAHK/F\n7pDFRgh2AADAGoIdQADS0tJMJtOxE2PFNVAwJRYAANhCsAMITGFhYaPDafN6+x483G3T6/VZ\nWVmsqgIAACAEO4BAjRo1SiCq7NNo5/IJtTZ7QUEBx3EMCwMAAECwAwiMuGlYZZ8VT6ptNl/P\nhmMAAAAMIdgBBEYMcFV9NhbDLrEAACATCHYAgcnLyyOiI7afW+xqbPbe4wAAAAwh2AEEJiUl\nxWg09g12R6wIdgAAIAsIdgABGzFiRIPd6RUE8T9r7Q6FQoEpsQAAwByCHUDARowY4RGEJodT\n/M86uyMzM1OhULCtCgAAQMm6gMjx+XxEZLVaLRYL61oguiUnJxNRvd2RpdNaPJ4ut2d0ejre\nV+Hg7Vkv0Gq1YjUZgGjhcrnE71yIvBgKduK3Asdx+HqAIcrIyCCiJoeLiMR2u4yMDLyvwqH3\nVcUnFwBAipgLdnq93mg0sq4FotvIkSOJqNnpIqIWp4uIsrOz8b4KB7vd7na7iUiv16OzGyBa\nqNVqnsdYLzbwugMELDU1lYhanU4ianG4eo8AAACwhWAHEDBxjF27y01E7W537xEAAAC2EOwA\nApaYmMhxXIfLTUSdLjcRJSUlsS4KAAAAwQ4gcEqlUq/XWzxeIuryeIgoPj6edVEAAAAIdgBB\nMZlM3R4PEVk9XiKKi4tjXREAAACCHUBQ9Hq9zeMlom63R/xP1hUBAAAg2AEERafTOXw+gcjp\n8ykUCrVazboiAAAABDuAoKjVap8guH0+h9en0WhYlwMAAECEYAcQHKVSSUQen+AVBPFnAAAA\n5hDsAIIhLqoucCT02fYKAACALQQ7gCERSGBdAgAAwFEIdgDB+P/27jUoqroP4Pjv7LJ3RFHi\nAVFRLCXLLDOyJnvKC+V4HbPS0cpmujjTfWqm1GqKHG0mS7tMOmI1WVONXaawkcouaJlKlBKW\nFxQDQVBBQLksy16eF6dnI1hwF2HPcvh+XrFn/8v83jDz5X/2nOP1ekXEIGIQRf0ZAADNEXZA\nZ7jdbhGJUhSzwaA+pR4AAM0RdkBnNDU1GRXFZDCYDQaXy6X1OAAAiBB2QOc0NDTYjAYRsUUZ\nvF6v0+nUeiIAAAg7oFPq6+vtRqOIOIxG9aXWEwEAQNgBnXLmzJk+pigRiY6KUl9qPREAAIQd\nEDqn0+l0OmNMUSLS1xQlIrW1tVoPBQAAYQeE7vTp0yLS32wWkX5mk/8IAADaIuyAkFVVVYlI\nf7NJROLMZhGprKzUeCYAAAg7oBNOnTolIhdYLCISZzH5jwAAoC3CDghZRUWFiMRbzSISb7WI\nyIkTJzSeCQAAwg7oBDXsEqwWEfmP1aKIlJeXaz0UAACEHRC60tJSEUmwmkXEYjDEmk1lZWVa\nDwUAAGEHhK60tNRmNMZZLOrLQXbbqVOnmpqatJ0KAADCDgiN1+stLS1NslmV/x8ZbLf6fL6S\nkhItxwIAgLADQlVRUeFyuQbbrf4jg202ESHsAACaI+yA0Pz1118ikmy3+Y8kO2z+4wAAaIiw\nA0Jz5MgREUl22P1HhjpsIlJUVKTZTAAAiAhhB4Tq6NGjIjIs+p8duySb1WwwEHYAAM0RdkBo\njhw5YlSUlqdijYqS7LAVFxd7PB4NBwMAgLADQuD1eo8ePTrIbjUb/vW3kxJtd7lcxcXFWg0G\nAIAQdkBIjh8/3tDQMLzFF+xUKQ67iBw+fFiLoQAA+BthB4SgsLBQRFKiHa2OD4+2y/+vqwAA\nQCuEHRACdU9OzbiWhkc7hB07AIDWCDsgBOqeXNuwi7eY+0RFsWMHANAWYQeE4MiRIzajMdFq\nafvWsGj78ePHGxsbwz8VAAAqwg4IlsvlKikpGeqwGRSl7bspDpvX6+VudgAADRF2QLDUO9UN\nbXNJrGoY108AALRG2AHB+vuZEw5bwHeH2nliLABAY4QdECw12lo+c6IldSePsAMAaIiwA4J1\n7NgxERnSzo7dAIvZbjSWlJSEdygAAP5B2AHBKikpMYgMtFkDvquIDLJby8rKvF5vmAcDAEBF\n2AHBKisr+4/VEhXokljVQKulubn5xIkT4ZwKAAA/wg4ISkNDQ01NTWI723WqgXabiJSXl4dr\nKAAA/oWwA4Jy8uRJEYm3mDtYc4HF7F8JAED4EXZAUE6dOiUicR2GnfquuhIAgPAj7ICgnD59\nWkT6m00drBlgNolIVVVVmGYCAODfCDsgKDU1NSLSr8Ow62cy+VcCABB+hB0QlLNnz4pIjCmq\ngzXqu+pKAADCj7ADgtLQ0CAidqOxgzU2o1FEGhsbwzQTAAD/RtgBQXE6nSJiMXT0J2MxGhTC\nDgCgnY7OKwHwc7lcIlLT3Hy80dnBsiiDQV0JAED4EXZAUHw+n4g8vnd/kCsBAAg/wg4IyqxZ\nsxobG4N5DuyNN94YhnkAAGiLsAOCctlll1122WVaTwEAQEe4eAIAAEAnCDsAAACdIOwAAAB0\ngrADAADQCcIOAABAJwg7AAAAnSDsAAAAdIKwAwAA0AnCDgAAQCcIOwAAAJ0g7AAAAHSCsAMA\nANAJwg4AAEAnCDsAAACdIOwAAAB0grADAADQCcIOAABAJ6K0HiBkXq/3iSeeOHz4sIi8+eab\ngwYN0noiAACAiNDzduw+//zzw4cPK4qi9SAAAACRpYeFXXl5+QcffDBhwgSbzab1LAAAAJGl\nJ4Wdz+d7/fXXzWbzvffeq/UsAAAAEacnhd1XX321b9++u+++u1+/flrPAgAAEHF6TNhVVla+\n++67l1566eTJk7WeBQAAIBL1mKti165d29zc/MADDwR52cTGjRtLS0tbHjEajSLS0NBQV1fX\nLSMC6Goej0f9oaGhgUumgJ7C5XJ5vV6tp+ilekbYbdu27ZdfflmwYEFSUlKQH/nxxx/37NnT\n8kh8fLyINDU1OZ3Orh8RQHdqamrSegQAwXK73YSdVnpA2NXW1mZmZg4ePPiWW245/9/222+/\nHTt27Px/DwAACKi4uFjrEXqvCAq7FStWuFwu/8tLL7107ty5IpKZmXn27Nlly5ZFRYUwbWZm\nZqsj5eXlM2bMWL9+fZdMCwAA2jN69GitR+ilIijs9u7d2/IkqcPhUH/Izc01m83vvfdey8Xq\nytWrV1sslrlz544dO/acvz8xMTEvL69LRwbQvTZu3Pjaa6+JSFZW1sCBA7UeBwAiXQSF3aZN\nm9p7q6mpad++fW2PFxYWisikSZO6cSwAAIAeIoLCrj0Bg2/evHkNDQ08KxYAAMCvx9zHDgAA\nAB0j7AAAAHSCsAMAANCJHvAdu4A++ugjrUcA0O0uuuiiOXPmSIvL5AEAHVB8Pp/WMwAAAKAL\ncCoWAABAJwg7AAAAnSDsAAAAdIKwAwAA0AnCDgAAQCcIOwAAAJ0g7AAAAHSCsAMAANAJwg5A\nxHG73fX19VpPAQA9D2EHILK43e4XX3zx6aefrqur03oWAOhhCDsAkeXtt9/Ozc09cuTIM888\nQ9sBQEgIOwAR5NixY9nZ2WazecCAAbQdAISKsAMQQd566y2PxzN79uwVK1bQdgAQKsIOQKSo\nrq7et29f//79586dm5iYSNsBQKgUn8+n9QwA8Lfff/+9trZ2woQJ6svy8vKlS5dWVVUNHz78\nhRdeiI6O1nY8AIhwhB2AiNZe2/35558XX3yxoijajgcAEYVTsQAiWsBzsjk5OUuXLl29ejX/\nmgJAS4QdgEjXqu2ys7PXrFnj9XoTExPZsQOAljgVC6Bn8J+TVV/Onz9//vz52o4EAJGGHTsA\nGnC73Tk5OU6nM/iPJCYmzpgxQ/2ZqgOAgKK0HgBAr6M+NCw3N9fhcKSnp0+fPv2CCy4456dy\ncnI2btwoVB0AtI9TsQDCraio6NFHHzWZTDab7cyZqkKoGQAACzJJREFUMwaD4Zprrpk1a1Zq\namp7H/ntt98yMjK8Xi9VBwAdMD733HNazwCgd4mNjc3Pzz9x4sSKFStSU1NPnjz5+++/b926\n9ddff7XZbIMGDTIYWn9LJDY2tqCgYMqUKVQdAHSAHTsAGti5c+fKlSvT09MffPBBEfnjjz82\nb968a9cur9cbFxc3ffr09PT0VrcjdrvdUVF8ewQAOkLYAdCAz+e77777qqur33nnnT59+qgH\nKysr33vvvR9++EFErFbrxIkTZ8yYkZSUpOmkANCTcCoWgAYURfH5fHl5edHR0aNGjVIP1tXV\nffTRR/X19UlJSY2Njfv379+yZUthYWG/fv0SEhK0HRgAegRudwJAG1OmTLHZbFu2bPF4PCJy\n8uTJpUuXnjx5csSIES+//PKGDRtuvfVWm82Wl5f3559/aj0sAPQM7NgB6EZut/v9998fOXJk\n26/HmUymmpqavXv3DhkyxGaz+asuIyPDbrdbLJYxY8bcfPPNI0eOnDp1qibDA0CPw3fsAHQX\n//3qFi1aNGfOnLYLKioq7r///qFDh9bX17esuvCPCgD6wKlYAN3CX3Ui8uWXX6rnW1tJSEgY\nP3780aNHqToA6BKEHYCu56+66OjolJSUysrKnTt3Blw5c+ZMEbFarc8//zxVBwDnibAD0MVa\nVt3y5csXLlwoIllZWQEXX3LJJcOHD3c6nVwhAQDnj7AD0JVaVV1KSsqVV16ZkJBw4MCBQ4cO\nBfzIrFmzROTjjz8O76QAoEOEHYAu07bqRERRlGnTponI5s2bA35qwoQJsbGxBw4cKCgoCOu4\nAKA7hB2ALlNVVVVYWNiy6lSTJ0+2Wq0//fTT6dOn237KaDSq5VdaWhq+WQFAj7iPHYAuEx0d\nPW7cuBtuuKFl1YmI2Ww+depUYWGhyWQaM2ZM2w8OGTIkOjpaPScLAOg07mMHIBxKSkoefPDB\nmJiYt99+22w2az0OAOgTp2IBhMOQIUNGjx595syZnJwcrWcBAN0i7AB0ntvt3rx5c5Ab/9On\nT5f2L6EAAJw/wg5AJ6nXwGZmZq5evTqYtrv66qvj4+OLi4vz8/PDMB4A9EKEHYBO2rNnj/rE\nsJycnGDazmAwTJ06VUS++OKLcMwHAL0PYQegky688EIRueaaa4xGY5Btl56ebjabf/3114D3\nPQEAnCfCDkAnxcbGxsfH9+3bd8mSJUG2XZ8+fa6//vp58+b1798/bHMCQO/BfewAdN7BgweL\nioruueeelJSUHTt2HD16tKKiYvz48YqiiIjH4zEYWv/3OHr06LFjx2oxLADoHzt2ADovNTW1\npKTE6XSmpaW13Lfzer0bNmx44YUXmpubW33EbrdrMioA9AaEHYDOS01N9Xq9hw4dEpGWbffA\nAw9kZWUVFBQUFRVpPSMA9CKEHYBzc7lcu3btavv9uZSUFLPZfPDgQfVlWlraU089pShKWVmZ\niCxZsmTkyJHhnhUAejHCDsA5/Pjjj/fee++KFStef/31Vm1nNBovvPBCf9iJSEFBgX/N9u3b\neWghAIQTYQegXR6P54033njppZeqq6vT0tKmT5+uXhXRUmpqqj/sNmzYkJWVZTKZbrvttuDv\ngQIA6CpRWg8AIEL5fL5Vq1bt2LHDarU+9NBDEyZMCLhs5MiRn332WUVFxZdffqlW3bJly8aO\nHTtixIiVK1cmJia2bUEAQDdR+GcaQEDvv//+pk2b7HZ7RkbGiBEj2ltWXV191113paSkFBUV\n+atOfausrCwpKSlc8wIAOBULIJDS0tJPP/1UUZQnn3yyg6oTkdjY2EGDBrWtOhGh6gAgzAg7\nAAFs2bLF4/Fcd911V1xxxTkXP/PMMwkJCa2qDgAQfnzHDkAA+fn5IvLf//43mMWJiYlr1641\nGo3dPBQA4BzYsQMQQE1NjYgE80TXn3/+2efzUXUAEAkIOwABxMTEiEh5eXnHyyorK1etWpWZ\nmRmWoQAA50DYAQhg1KhRIrJ169aOl2VmZrrdbofDEZahAADnQNgBCODmm29WFGXv3r3ff/99\ne2uysrJ27tzZt2/f2bNnh3M2AEB7CDsAAVx00UXp6eki8sYbb+zatavtgq1bt7711lsi8tBD\nD7FjBwARghsUAwisubn52Wef/eOPPxRFmTlz5ty5c/v27SsiVVVVH3744TfffCMiixYtmjNn\njtaTAgD+RtgBaJfT6Vy1alVubq6IGI3GgQMHer3e48eP+3w+g8Fw//33T506VesZAQD/IOyA\nXsrtdu/fv3/06NEdL/P5fN99992mTZsqKirUI4qiXH755YsWLRo2bFj3jwkACAFhB/RGHo9n\n5cqVeXl5jz32WJB3IS4uLj5x4oTZbE5OTo6Nje3uCQEAncCTJ4DeyGAwDBgwwOv1rl69WoJ7\nwkRycnJycnL3jwYA6Dzjc889p/UMAMJNUZRx48bV1tYWFhbu3r07MTFx6NChWg8FADhfhB3Q\nS4XUdrt3746JibFYLGEcEAAQMsIO6L2CbLucnJxVq1bl5eVNmjQpKorvbwBA5OIGxUBvUV1d\nvX79+m+//bblQUVRFi9ePHXqVPX7dtu2bWv1qZycnDVr1ni93muvvZYdOwCIcPzzDfQKWVlZ\nGzdudLlcDodj1KhRAwcO9L+ltp2IZGdnt7qWwl918+fPnz9/viaTAwCCR9gBOufxeF599dWc\nnBxFUWbPnj1v3jy73d5qTcC2o+oAoMfhPnaAzq1duzY7O9vhcDz++OPjxo3rYKXP51u3bl12\ndrbBYLjpppu+/vprqg4AehbCDtCzgoKCZcuWGY3GjIyMcz5kQlq0nfqSqgOAnoVTsYCeqYk2\nefLkVlXncrm2b9+en58vImlpadddd52iKPLvc7JUHQD0OIQdoGclJSUiMmbMmJYHc3Nz161b\nV1lZqb7ctm1bXl7eo48+2rLtrrrqqo7P2wIAIhC3OwH0TH2o6759+9SXtbW1r7zyyvLlyysr\nK8ePH//www/PnDlTUZQffvjhm2++8X9Kvb+dNhMDAM4DO3aAnk2bNi0/P3/Lli1nzpyJiYnZ\nvn17XV1dXFzcI4884t/G8/l8mzdv3rZt20033aTttACA80TYAXo2fvz4BQsWfPDBBz/99JOI\nGI3GadOm3XHHHS3veKLe0665uVmzKQEAXYSwA3Tu9ttvT0tL27Nnj8PhuOqqq/r3799qgfq0\niVGjRmkxHQCgKxF2gP4NGzZs2LBhAd/6/PPPDxw4YLVaZ8yYEeapAABdjosngN4rKyvrnXfe\nEZHFixfHxcVpPQ4A4HyxYwf0RnV1dRs2bPj+++9F5M4775w4caLWEwEAugBhB/QuPp/vs88+\n++STT+rr661W6+LFi6k6ANANwg7oXRRFiYqKamhomDhx4sKFCzkDCwB6wrNigd6ourpavXcx\nAEBPCDsAAACd4KpYAAAAnSDsAAAAdIKwAwAA0AnCDgAAQCcIOwAAAJ0g7AAAAHSCsAMAANAJ\nwg4AAEAnCDsAAACdIOwAAAB0grADAADQCcIOAABAJwg7AAAAnSDsAAAAdIKwAwAA0AnCDgAA\nQCcIOwAAAJ0g7AAAAHSCsAMAANAJwg4AAEAnCDsAAACdIOwAAAB0grADAADQCcIOAABAJwg7\nAAAAnSDsAAAAdIKwAwAA0AnCDgAAQCcIOwAAAJ0g7AAAAHSCsAMAANAJwg4AAEAnCDsAAACd\nIOwAAAB0grADAADQCcIOAABAJwg7AAAAnSDsAAAAdIKwAwAA0AnCDgAAQCcIOwAAAJ0g7AAA\nAHSCsAMAANAJwg4AAEAnCDsAAACdIOwAAAB0grADAADQCcIOAABAJ/4H3VwbmetPVkEAAAAA\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=7, repr.plot.height=7)\n",
    "tmp.KIRP_race_age_gen_n <- which(KIRP_clin_data_tumor_filt_all_n$ajcc_pathologic_n %in% c(\"N0\", \"N1\"))\n",
    "tmp.KIRP_race_age_gen_n_sig <- rownames(KIRP_race_age_gen_n_topTable_ADME)[KIRP_race_age_gen_n_topTable_ADME$Symbol %in% c(\"SLC47A1\", \"ABCG2\")] \n",
    "KIRP_race_age_gen_n_inp <- do.call(rbind.data.frame, lapply(tmp.KIRP_race_age_gen_n_sig, function(x)  {\n",
    "        cbind.data.frame(KIRP_clin_data_tumor_filt_all_n[tmp.KIRP_race_age_gen_n,],\n",
    "                         Gene=rep(KIRP_race_age_gen_n_topTable_ADME$Symbol[rownames(KIRP_race_age_gen_n_topTable_ADME) %in% x], \n",
    "                              nrow(KIRP_clin_data_tumor_filt_all_n[tmp.KIRP_race_age_gen_n,])),\n",
    "                         Value=as.numeric(KIRP_voom_all_n$E[rownames(KIRP_voom_all_n) %in% x, tmp.KIRP_race_age_gen_n]))\n",
    "    }))\n",
    "KIRP_race_age_gen_n_inp$Gene <- factor(as.character(KIRP_race_age_gen_n_inp$Gene), \n",
    "                               levels=c(\"ABCG2\",\"SLC47A1\"))                                                            \n",
    "\n",
    "ggplot(KIRP_race_age_gen_n_inp, aes(x=Gene, y=Value, fill=ajcc_pathologic_n)) + geom_violin() +\n",
    "    theme_bw(base_size=16) + ylab(\"Log2(CPM)\") + labs(title=\"KIRP - Lymph Node Progression\") + xlab(\"\") +\n",
    "    theme(legend.title=element_text(size=0)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n",
    "    stat_summary(fun = \"median\", geom='point', inherit.aes=TRUE, position=position_dodge(0.95)) + scale_color_brewer(palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59532fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "KIRP_n_network_inp <- read.csv(file=\"Nigam_topTable_KIRP_ajcc_path_n_DE_brinADMEgenes_update_04212022.txt\", sep=\"\\t\", header=TRUE)\n",
    "KIRP_n_network_inp_sig <- KIRP_n_network_inp[KIRP_n_network_inp$adj.P.Val < 0.1,]\n",
    "KIRP_n_network_inp_sig_exp <- KIRP_voom_race_age_gen$E[rownames(KIRP_voom_race_age_gen$E) %in% \n",
    "                                                       rownames(KIRP_n_network_inp_sig),]\n",
    "rownames(KIRP_n_network_inp_sig_exp) <- KIRP_n_network_inp_sig$Symbol[match(rownames(KIRP_n_network_inp_sig_exp),\n",
    "                                                                                rownames(KIRP_n_network_inp_sig))]\n",
    "\n",
    "KIRP_clin_data_tumor_ML_inp <- KIRP_clin_data_tumor_filt_race_age_gen[,c(2,4,48), drop=FALSE]\n",
    "KIRP_clin_data_tumor_ML_inp$gender <- as.numeric(KIRP_clin_data_tumor_filt_race_age_gen$gender)-1\n",
    "KIRP_clin_data_tumor_ML_inp$status <- as.numeric(factor(KIRP_clin_data_tumor_filt_race_age_gen$vital_status))-1\n",
    "KIRP_clin_data_tumor_ML_inp$days_to_last_follow_up[KIRP_clin_data_tumor_ML_inp$status == \n",
    "        1] <- KIRP_clin_data_tumor_filt_race_age_gen$days_to_death[match(KIRP_clin_data_tumor_ML_inp$case_submitter_id[\n",
    "    KIRP_clin_data_tumor_ML_inp$status == 1], KIRP_clin_data_tumor_filt_race_age_gen$case_submitter_id)]\n",
    "KIRP_clin_data_tumor_ML_inp$race_white <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$race == \"white\", 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$race_black <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$race == \"black\", 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$race_asian <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$race == \"asian\", 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$race_NR <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$race == \"not_reported\", 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$t_T1 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_t %in% c(\"T1\", \"T1a\", \"T1b\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$t_T2 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_t %in% c(\"T2\", \"T2a\", \"T2b\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$t_T3 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_t %in% c(\"T3\", \"T3a\", \"T3b\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$t_T4 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_t %in% c(\"T4\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$t_T1 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_t %in% c(\"T1\", \"T1a\", \"T1b\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$m_M0 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_m %in% c(\"M0\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$m_M1 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_m %in% c(\"M1\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$m_MX <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_m %in% c(\"MX\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$n_N0 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_n %in% c(\"N0\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$n_N1 <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_n %in% c(\"N1\"), 1, 0)\n",
    "KIRP_clin_data_tumor_ML_inp$n_NX <- ifelse(KIRP_clin_data_tumor_filt_race_age_gen$ajcc_pathologic_n %in% c(\"NX\"), 1, 0)\n",
    "KIRP_clin_data_sigDE_n_network_inp_exp <- cbind.data.frame(KIRP_clin_data_tumor_ML_inp, \n",
    "                                                          as.data.frame(t(KIRP_n_network_inp_sig_exp)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "30c956c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Network File, NOT RUN\n",
    "write.csv(KIRP_clin_data_sigDE_n_network_inp_exp, file=\"Nigam_TCGA_KIRP_n_network_inp_clin_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-response",
   "metadata": {},
   "source": [
    "## Survival Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "compact-plasma",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in data.frame(status = as.numeric(as.factor(KIRP_clin_data_tumor_filt$vital_status)), :\n",
      "“NAs introduced by coercion”\n",
      "Warning message in data.frame(status = as.numeric(as.factor(KIRP_clin_data_tumor_filt$vital_status)), :\n",
      "“NAs introduced by coercion”\n"
     ]
    }
   ],
   "source": [
    "KIRP_surv_inp <- data.frame(status=as.numeric(as.factor(KIRP_clin_data_tumor_filt$vital_status)), \n",
    "                            time=as.numeric(KIRP_clin_data_tumor_filt$days_to_last_follow_up),\n",
    "                            age=KIRP_clin_data_tumor_filt$age_at_index,\n",
    "                            age2=as.numeric(KIRP_clin_data_tumor_filt$age_at_diagnosis),\n",
    "                            gender=as.numeric(KIRP_clin_data_tumor_filt$gender))\n",
    "KIRP_surv_inp$time[which(KIRP_surv_inp$status == 2)] <- as.numeric(KIRP_clin_data_tumor_filt$days_to_death[which(KIRP_surv_inp$status == 2)])\n",
    "rownames(KIRP_surv_inp) <- KIRP_clin_data_tumor_filt$case_submitter_id\n",
    "KIRP_surv_inp$Stage1 <- KIRP_surv_inp$Stage2 <- KIRP_surv_inp$Stage3 <- KIRP_surv_inp$Stage4 <- KIRP_surv_inp$M0 <- \n",
    "KIRP_surv_inp$M1 <- KIRP_surv_inp$N0 <- KIRP_surv_inp$N1 <- KIRP_surv_inp$T1 <- KIRP_surv_inp$T2 <- \n",
    "KIRP_surv_inp$T3 <- KIRP_surv_inp$RaceWhite <- KIRP_surv_inp$RaceBlack <- KIRP_surv_inp$RaceAsian <- \n",
    "KIRP_surv_inp$PriMalig <- KIRP_surv_inp$SecMalig <- 0\n",
    "KIRP_surv_inp$Stage1[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage) == \"StageI\")] <- 1\n",
    "KIRP_surv_inp$Stage2[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage) == \"StageII\")] <- 1\n",
    "KIRP_surv_inp$Stage3[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage) == \"StageIII\")] <- 1\n",
    "KIRP_surv_inp$Stage4[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_stage) == \"StageIV\")] <- 1\n",
    "KIRP_surv_inp$Stage <- factor(as.character(KIRP_clin_data_tumor_filt$ajcc_clinical_stage))\n",
    "KIRP_surv_inp$M0[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_m) == \"M0\")] <- 1\n",
    "KIRP_surv_inp$M1[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_m) == \"M1\")] <- 1\n",
    "KIRP_surv_inp$M <- factor(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_m))\n",
    "KIRP_surv_inp$N0[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_n) == \"N0\")] <- 1\n",
    "KIRP_surv_inp$N1[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_n) == \"N1\")] <- 1\n",
    "KIRP_surv_inp$N <- factor(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_n))\n",
    "KIRP_surv_inp$T1[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_t) %in% c(\"T1\", \"T1a\", \"T1b\"))] <- 1\n",
    "KIRP_surv_inp$T2[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_t) %in% c(\"T2\", \"T2a\", \"T2b\"))] <- 1\n",
    "KIRP_surv_inp$T3[which(as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_t) %in% c(\"T3\", \"T3a\", \"T3b\", \"T3c\"))] <- 1\n",
    "KIRP_surv_inp$T <- factor(gsub(\"T3a|T3b|T3c\", \"T3\", gsub(\"T2a|T2b\", \"T2\", \n",
    "                                gsub(\"T1a|T1b\", \"T1\", as.character(KIRP_clin_data_tumor_filt$ajcc_pathologic_t)))))\n",
    "KIRP_surv_inp$RaceWhite[which(as.character(KIRP_clin_data_tumor_filt$race) == \"white\")] <- 1\n",
    "KIRP_surv_inp$RaceBlack[which(as.character(KIRP_clin_data_tumor_filt$race) == \"black or african american\")] <- 1\n",
    "KIRP_surv_inp$RaceAsian[which(as.character(KIRP_clin_data_tumor_filt$race) == \"asian\")] <- 1\n",
    "KIRP_surv_inp$PriMalig[which(as.character(KIRP_clin_data_tumor_filt$prior_malignancy) == \"yes\")] <- 1\n",
    "KIRP_surv_inp$TreatmentType <- factor(gsub(\", NOS$\", \"\", KIRP_clin_data_tumor_filt$treatment_type))\n",
    "KIRP_surv_inp$Race <- factor(gsub(\" or african american$\", \"\", as.character(KIRP_clin_data_tumor_filt$race)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "still-steering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>17</li><li>289</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 17\n",
       "\\item 289\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 17\n",
       "2. 289\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  17 289"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KIRP_data_tumor_SLC22 <- KIRP_data_tumor_filt_orig[match(all_SLC22_nodes_KIRP.df.filt$gene_id, \n",
    "                                                    rownames(KIRP_data_tumor_filt_orig)),]\n",
    "\n",
    "KIRP_invNormTrans <- apply(KIRP_data_tumor_SLC22, 1, function(x) {\n",
    "    round(qnorm((rank(x, na.last=\"keep\")-0.5)/sum(!is.na(x))), digit=5)})\n",
    "KIRP_invNormTrans.df <- as.data.frame(t(KIRP_invNormTrans)) ##(rows are genes, cols are samples)\n",
    "dim(KIRP_invNormTrans.df)\n",
    "KIRP_invNormTrans.df <- KIRP_invNormTrans.df[,match(rownames(KIRP_surv_inp),\n",
    "                                    unlist(lapply(strsplit(colnames(KIRP_invNormTrans.df), split=\"\\\\.\"), function(x) {\n",
    "                                            paste(x[[1]], x[[2]], x[[3]], sep=\"-\")\n",
    "                                    })))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "boolean-professional",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.4495384    0.6379226 6.486506e-03\n",
      "SLC22A2  -0.9101868    0.4024490 1.313624e-06\n",
      "SLC22A24 -0.5808486    0.5594234 7.492910e-04\n",
      "[1] 0.005739828 0.005172514 0.016189459\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression and no factors\n",
    "KIRP_coxph.out_nofac <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender, data=inv.df)\n",
    "    KIRP_coxph.out_nofac[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_nofac.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_nofac, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_nofac.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_nofac.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "\n",
    "if(sum (KIRP_coxph.coef.pval_nofac.df$pval < 0.01) > 0) {\n",
    "w_KIRP_cox_nofac <- which(KIRP_coxph.coef.pval_nofac.df$pval < 0.01)\n",
    "show(KIRP_coxph.coef.pval_nofac.df[w_KIRP_cox_nofac,])\n",
    "                                                                  \n",
    "KIRP_cox_nofac_diag <- lapply(KIRP_coxph.out_nofac[w_KIRP_cox_nofac], function(x) cox.zph(x))\n",
    "KIRP_cox_nofac_diag_glob_p <- unlist(lapply(KIRP_cox_nofac_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_nofac_diag_glob_p)\n",
    "w_KIRP_cox_nofac_diag <- which(KIRP_cox_nofac_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sonic-polymer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard_ratio</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SLC22A18</th><td>-0.5529791</td><td>0.5752336</td><td>4.015022e-04</td></tr>\n",
       "\t<tr><th scope=row>SLC22A2</th><td>-0.7817740</td><td>0.4575935</td><td>3.550625e-05</td></tr>\n",
       "\t<tr><th scope=row>SLC22A24</th><td>-0.6141615</td><td>0.5410944</td><td>7.279835e-04</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & coef & hazard\\_ratio & pval\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tSLC22A18 & -0.5529791 & 0.5752336 & 4.015022e-04\\\\\n",
       "\tSLC22A2 & -0.7817740 & 0.4575935 & 3.550625e-05\\\\\n",
       "\tSLC22A24 & -0.6141615 & 0.5410944 & 7.279835e-04\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 3\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard_ratio &lt;dbl&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| SLC22A18 | -0.5529791 | 0.5752336 | 4.015022e-04 |\n",
       "| SLC22A2 | -0.7817740 | 0.4575935 | 3.550625e-05 |\n",
       "| SLC22A24 | -0.6141615 | 0.5410944 | 7.279835e-04 |\n",
       "\n"
      ],
      "text/plain": [
       "         coef       hazard_ratio pval        \n",
       "SLC22A18 -0.5529791 0.5752336    4.015022e-04\n",
       "SLC22A2  -0.7817740 0.4575935    3.550625e-05\n",
       "SLC22A24 -0.6141615 0.5410944    7.279835e-04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.000498756809743234</li><li>0.0011311513114609</li><li>0.00168801892499189</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.000498756809743234\n",
       "\\item 0.0011311513114609\n",
       "\\item 0.00168801892499189\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.000498756809743234\n",
       "2. 0.0011311513114609\n",
       "3. 0.00168801892499189\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 0.0004987568 0.0011311513 0.0016880189"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression and base factors (age, gender)\n",
    "KIRP_coxph.out_base <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + Stage1 + Stage2 + Stage3 + Stage4 + age2 + gender, data=inv.df)\n",
    "    KIRP_coxph.out_base[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_base.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_base, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_base.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_base.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "\n",
    "w_KIRP_cox_base <- which(KIRP_coxph.coef.pval_base.df$pval < 0.01)\n",
    "KIRP_coxph.coef.pval_base.df[w_KIRP_cox_base,]\n",
    "                                                                  \n",
    "KIRP_cox_base_diag <- lapply(KIRP_coxph.out_base[w_KIRP_cox_base], function(x) cox.zph(x))\n",
    "KIRP_cox_base_diag_glob_p <- unlist(lapply(KIRP_cox_base_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "KIRP_cox_base_diag_glob_p\n",
    "w_KIRP_cox_base_diag <- which(KIRP_cox_base_diag_glob_p < 0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb1be182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard_ratio</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SLC22A18</th><td>-0.4555792</td><td>0.6340806</td><td>5.443624e-03</td></tr>\n",
       "\t<tr><th scope=row>SLC22A2</th><td>-0.8852318</td><td>0.4126185</td><td>2.829581e-06</td></tr>\n",
       "\t<tr><th scope=row>SLC22A13</th><td>-0.4244354</td><td>0.6541390</td><td>7.849828e-03</td></tr>\n",
       "\t<tr><th scope=row>SLC22A24</th><td>-0.6056222</td><td>0.5457347</td><td>5.743739e-04</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & coef & hazard\\_ratio & pval\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tSLC22A18 & -0.4555792 & 0.6340806 & 5.443624e-03\\\\\n",
       "\tSLC22A2 & -0.8852318 & 0.4126185 & 2.829581e-06\\\\\n",
       "\tSLC22A13 & -0.4244354 & 0.6541390 & 7.849828e-03\\\\\n",
       "\tSLC22A24 & -0.6056222 & 0.5457347 & 5.743739e-04\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 3\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard_ratio &lt;dbl&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| SLC22A18 | -0.4555792 | 0.6340806 | 5.443624e-03 |\n",
       "| SLC22A2 | -0.8852318 | 0.4126185 | 2.829581e-06 |\n",
       "| SLC22A13 | -0.4244354 | 0.6541390 | 7.849828e-03 |\n",
       "| SLC22A24 | -0.6056222 | 0.5457347 | 5.743739e-04 |\n",
       "\n"
      ],
      "text/plain": [
       "         coef       hazard_ratio pval        \n",
       "SLC22A18 -0.4555792 0.6340806    5.443624e-03\n",
       "SLC22A2  -0.8852318 0.4126185    2.829581e-06\n",
       "SLC22A13 -0.4244354 0.6541390    7.849828e-03\n",
       "SLC22A24 -0.6056222 0.5457347    5.743739e-04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.0318767668274178</li><li>0.0384855128833345</li><li>0.0360247813353232</li><li>0.0435049650224503</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.0318767668274178\n",
       "\\item 0.0384855128833345\n",
       "\\item 0.0360247813353232\n",
       "\\item 0.0435049650224503\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.0318767668274178\n",
       "2. 0.0384855128833345\n",
       "3. 0.0360247813353232\n",
       "4. 0.0435049650224503\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 0.03187677 0.03848551 0.03602478 0.04350497"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + Race, data=inv.df)\n",
    "    KIRP_coxph.out_race[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_race, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_race.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "\n",
    "w_KIRP_cox_race <- which(KIRP_coxph.coef.pval_race.df$pval < 0.01)\n",
    "KIRP_coxph.coef.pval_race.df[w_KIRP_cox_race,]\n",
    "                                                                  \n",
    "KIRP_cox_race_diag <- lapply(KIRP_coxph.out_race[w_KIRP_cox_race], function(x) cox.zph(x))\n",
    "KIRP_cox_race_diag_glob_p <- unlist(lapply(KIRP_cox_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "KIRP_cox_race_diag_glob_p\n",
    "w_KIRP_cox_race_diag <- which(KIRP_cox_race_diag_glob_p < 0.05)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "colored-institution",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.5686457    0.5662919 0.0002787875\n",
      "SLC22A2  -0.7520255    0.4714107 0.0000591176\n",
      "SLC22A24 -0.6359888    0.5294118 0.0005906174\n",
      "[1] 0.001653148 0.003686694 0.004874301\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_base_race2 <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + Stage1 + Stage2 + Stage3 + Stage4 + \n",
    "                              age2 + gender + RaceWhite + RaceBlack + RaceAsian, data=inv.df)\n",
    "    KIRP_coxph.out_base_race2[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval.base_race2.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_base_race2, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval.base_race2.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval.base_race2.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "if(sum(KIRP_coxph.coef.pval.base_race2.df$pval < 0.01) > 0) {\n",
    "w_KIRP_cox_base_race2 <- which(KIRP_coxph.coef.pval.base_race2.df$pval < 0.01)\n",
    "show(KIRP_coxph.coef.pval.base_race2.df[w_KIRP_cox_base_race2,])\n",
    "                                                                  \n",
    "KIRP_cox_base_race2_diag <- lapply(KIRP_coxph.out_base_race2[w_KIRP_cox_base_race2], function(x) cox.zph(x))\n",
    "KIRP_cox_base_race2_diag_glob_p <- unlist(lapply(KIRP_cox_base_race2_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_base_race2_diag_glob_p)\n",
    "w_KIRP_cox_base_race2_diag <- which(KIRP_cox_base_race2_diag_glob_p < 0.05)\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "banned-exercise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.4597203    0.6314602 5.643067e-03\n",
      "SLC22A2  -0.9105052    0.4023209 1.096492e-06\n",
      "SLC22A24 -0.5916532    0.5534116 6.834294e-04\n",
      "[1] 0.007026719 0.005521878 0.010139356\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :\n",
      "“Ran out of iterations and did not converge”\n",
      "Warning message in sqrt(diag(cox$var)):\n",
      "“NaNs produced”\n",
      "Warning message in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :\n",
      "“Ran out of iterations and did not converge”\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, primary malignancy, and base factors (age, gender)\n",
    "KIRP_coxph.out_priMalig <- KIRP_coxph.mod_priMalig <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- summary(coxph(Surv(time, status) ~ gene + age2 + gender + PriMalig, data=inv.df))\n",
    "    tmp.mod <- coxph(Surv(time, status) ~ gene + age2 + gender + PriMalig, data=inv.df)\n",
    "    KIRP_coxph.out_priMalig[[i]] <- tmp.surv\n",
    "    KIRP_coxph.mod_priMalig[[i]] <- tmp.mod\n",
    "}\n",
    "KIRP_coxph.coef.pval_priMalig.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_priMalig, function(x) x$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_priMalig.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_priMalig.df) <- c(\"coef\", \"hazard_ratio\", \"pval\")\n",
    "                                                                     \n",
    "if(sum(KIRP_coxph.coef.pval_priMalig.df$pval < 0.01) > 0) {\n",
    "w_KIRP_cox_priMalig <- which(KIRP_coxph.coef.pval_priMalig.df$pval < 0.01)\n",
    "show(KIRP_coxph.coef.pval_priMalig.df[w_KIRP_cox_priMalig,])\n",
    "                                                                  \n",
    "KIRP_cox_priMalig_diag <- lapply(KIRP_coxph.mod_priMalig[w_KIRP_cox_priMalig], function(x) cox.zph(x))\n",
    "KIRP_cox_priMalig_diag_glob_p <- unlist(lapply(KIRP_cox_priMalig_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_priMalig_diag_glob_p)\n",
    "w_KIRP_cox_priMalig_diag <- which(KIRP_cox_priMalig_diag_glob_p < 0.05)\n",
    "}\n",
    "                                 \n",
    "#KIRP_cox_priMalig_diag\n",
    "SLC22A2.KIRP.inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[5,]))          \n",
    "SLC22A2.KIRP.priMalig.surv <- summary(coxph(Surv(time, status) ~ gene + age2 + gender + PriMalig, \n",
    "                                            data=SLC22A2.KIRP.inv.df))\n",
    "SLC22A2.KIRP.priMalig.mod <- coxph(Surv(time, status) ~ gene + age2 + gender + PriMalig, \n",
    "                                   data=SLC22A2.KIRP.inv.df)\n",
    "SLC22A2.KIRP.priMalig.surv2 <- summary(coxph(Surv(time, status) ~ gene + strata(age2) + gender + PriMalig, \n",
    "                                            data=SLC22A2.KIRP.inv.df))\n",
    "SLC22A2.KIRP.priMalig.mod2 <- coxph(Surv(time, status) ~ gene + strata(age2) + gender + PriMalig, \n",
    "                                   data=SLC22A2.KIRP.inv.df)\n",
    "\n",
    "                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c61daf6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.4671379    0.6267937 4.542739e-03\n",
      "SLC22A2  -0.8863224    0.4121688 2.294701e-06\n",
      "SLC22A13 -0.4317372    0.6493800 6.976518e-03\n",
      "SLC22A23  0.4219007    1.5248570 9.939883e-03\n",
      "SLC22A24 -0.6179197    0.5390647 5.080191e-04\n",
      "[1] 0.03212799 0.03297396 0.02064991 0.03805713 0.02320433\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, primary malignancy, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_priMalig_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + \n",
    "                              PriMalig + Race, data=inv.df)\n",
    "    KIRP_coxph.out_priMalig_race[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_priMalig_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_priMalig_race, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_priMalig_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_priMalig_race.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "                                                                          \n",
    "if(sum (KIRP_coxph.coef.pval_priMalig_race.df$pval < 0.01) > 0) {\n",
    "w_KIRP_cox_priMalig_race <- which(KIRP_coxph.coef.pval_priMalig_race.df$pval < 0.01)\n",
    "show(KIRP_coxph.coef.pval_priMalig_race.df[w_KIRP_cox_priMalig_race,])\n",
    "                                                                  \n",
    "KIRP_cox_priMalig_race_diag <- lapply(KIRP_coxph.out_priMalig_race[w_KIRP_cox_priMalig_race], function(x) cox.zph(x))\n",
    "KIRP_cox_priMalig_race_diag_glob_p <- unlist(lapply(KIRP_cox_priMalig_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_priMalig_race_diag_glob_p)\n",
    "w_KIRP_cox_priMalig_race_diag <- which(KIRP_cox_priMalig_race_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dynamic-dinner",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.4485935    0.6385256 6.722625e-03\n",
      "SLC22A2  -0.9087330    0.4030346 1.376105e-06\n",
      "SLC22A24 -0.5801044    0.5598399 7.394708e-04\n",
      "[1] 0.01124940 0.01102998 0.02786368\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, treatment type, and base factors (age, gender)\n",
    "KIRP_coxph.out_treatment <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + TreatmentType, data=inv.df)\n",
    "    KIRP_coxph.out_treatment[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_treatment.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_treatment, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_treatment.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_treatment.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "                                                                     \n",
    "if(sum (KIRP_coxph.coef.pval_treatment.df$pval < 0.01) > 0) {\n",
    "w_KIRP_cox_treatment <- which(KIRP_coxph.coef.pval_treatment.df$pval < 0.01)\n",
    "show(KIRP_coxph.coef.pval_treatment.df[w_KIRP_cox_treatment,])\n",
    "                                                                  \n",
    "KIRP_cox_treatment_diag <- lapply(KIRP_coxph.out_treatment[w_KIRP_cox_treatment], function(x) cox.zph(x))\n",
    "KIRP_cox_treatment_diag_glob_p <- unlist(lapply(KIRP_cox_treatment_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_treatment_diag_glob_p)\n",
    "w_KIRP_cox_treatment_diag <- which(KIRP_cox_treatment_diag_glob_p < 0.01)\n",
    "}\n",
    "                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "445ce9fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              coef hazard_ratio         pval\n",
      "SLC22A2 -0.8841183    0.4130782 2.876815e-06\n",
      "[1] 0.06008774\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, treatment type, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_treatment_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + TreatmentType + Race, data=inv.df)\n",
    "    KIRP_coxph.out_treatment_race[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_treatment_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_treatment_race, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_treatment_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_treatment_race.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "                                                                                                                                                \n",
    "if(sum (KIRP_coxph.coef.pval_treatment_race.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_treatment_race <- which(KIRP_coxph.coef.pval_treatment_race.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_treatment_race.df[w_KIRP_cox_treatment_race,])\n",
    "                                                                  \n",
    "KIRP_cox_treatment_race_diag <- lapply(KIRP_coxph.out_treatment_race[w_KIRP_cox_treatment_race], function(x) cox.zph(x))\n",
    "KIRP_cox_treatment_race_diag_glob_p <- unlist(lapply(KIRP_cox_treatment_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_treatment_race_diag_glob_p)\n",
    "w_KIRP_cox_treatment_race_diag <- which(KIRP_cox_treatment_race_diag_glob_p < 0.01)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "complimentary-calvin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A2  -0.8608859    0.4227874 1.695237e-06\n",
      "SLC22A24 -0.7178532    0.4877984 4.989184e-05\n",
      "[1] 0.001727426 0.003243918\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, pathologic stage, and base factors (age, gender)\n",
    "KIRP_coxph.out_stage <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + Stage, \n",
    "                              data=inv.df)\n",
    "    KIRP_coxph.out_stage[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_stage.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_stage, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_stage.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_stage.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "                                                                                                                                            \n",
    "if(sum (KIRP_coxph.coef.pval_stage.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_stage <- which(KIRP_coxph.coef.pval_stage.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_stage.df[w_KIRP_cox_stage,])\n",
    "                                                                  \n",
    "KIRP_cox_stage_diag <- lapply(KIRP_coxph.out_stage[w_KIRP_cox_stage], function(x) cox.zph(x))\n",
    "KIRP_cox_stage_diag_glob_p <- unlist(lapply(KIRP_cox_stage_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_stage_diag_glob_p)\n",
    "w_KIRP_cox_stage_diag <- which(KIRP_cox_stage_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa5d9285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.5370928    0.5844449 3.296065e-04\n",
      "SLC22A2  -0.8513621    0.4268331 1.434635e-06\n",
      "SLC22A24 -0.7573798    0.4688934 3.263103e-05\n",
      "   SLC22A18     SLC22A2    SLC22A24 \n",
      "0.005139539 0.007730497 0.008719193 \n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, pathologic stage, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_stage_race <- KIRP_coxph.mod_stage_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- summary(coxph(Surv(time, status) ~ gene + age2 + gender + Stage + Race, data=inv.df))\n",
    "    tmp.mod <- coxph(Surv(time, status) ~ gene + age2 + gender + Stage + Race, data=inv.df)\n",
    "    KIRP_coxph.out_stage_race[[i]] <- tmp.surv\n",
    "    KIRP_coxph.mod_stage_race[[i]] <- tmp.mod\n",
    "}\n",
    "KIRP_coxph.coef.pval_stage_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_stage_race, function(x) x$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_stage_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_stage_race.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "names(KIRP_coxph.mod_stage_race) <- names(KIRP_coxph.out_stage_race) <- rownames(KIRP_coxph.coef.pval_stage_race.df)\n",
    "                                                                       \n",
    "                                                                                                                                                 \n",
    "if(sum (KIRP_coxph.coef.pval_stage_race.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_stage_race <- which(KIRP_coxph.coef.pval_stage_race.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_stage_race.df[w_KIRP_cox_stage_race,])\n",
    "                                                                  \n",
    "KIRP_cox_stage_race_diag <- lapply(KIRP_coxph.mod_stage_race[w_KIRP_cox_stage_race], function(x) cox.zph(x))\n",
    "KIRP_cox_stage_race_diag_glob_p <- unlist(lapply(KIRP_cox_stage_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_stage_race_diag_glob_p)\n",
    "w_KIRP_cox_stage_race_diag <- which(KIRP_cox_stage_race_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "loaded-bennett",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "17"
      ],
      "text/latex": [
       "17"
      ],
      "text/markdown": [
       "17"
      ],
      "text/plain": [
       "[1] 17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              coef hazard_ratio        pval\n",
      "SLC22A2 -0.8844331    0.4129482 1.16886e-05\n",
      "[1] 0.005532825\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, presence of metastasis, and base factors (age, gender)\n",
    "KIRP_coxph.out_m <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <-coxph(Surv(time, status) ~ gene + age2 + gender + M0 + M1, \n",
    "                              data=inv.df)\n",
    "    KIRP_coxph.out_m[[i]] <- tmp.surv\n",
    "}\n",
    "length(KIRP_coxph.out_m)\n",
    "#KIRP_coxph.coef.pval_m.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_m, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "KIRP_coxph.coef.pval_m.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_m, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_m.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_m.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "                                                                                                                                                 \n",
    "if(sum (KIRP_coxph.coef.pval_m.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_m <- which(KIRP_coxph.coef.pval_m.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_m.df[w_KIRP_cox_m,])\n",
    "                                                                  \n",
    "KIRP_cox_m_diag <- lapply(KIRP_coxph.out_m[w_KIRP_cox_m], function(x) cox.zph(x))\n",
    "KIRP_cox_m_diag_glob_p <- unlist(lapply(KIRP_cox_m_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_m_diag_glob_p)\n",
    "w_KIRP_cox_m_diag <- which(KIRP_cox_m_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "14f97d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              coef hazard_ratio         pval\n",
      "SLC22A2 -0.8445918    0.4297328 2.523022e-05\n",
      "[1] 0.01714071\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, presence of metastasis, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_m_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + M0 + M1 + Race, \n",
    "                              data=inv.df)\n",
    "    KIRP_coxph.out_m_race[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_m_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_m_race, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "#KIRP_coxph.coef.pval_m_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_m_race, function(x) x$coefficients[c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_m_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_m_race.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "                                                                                                                                                 \n",
    "if(sum (KIRP_coxph.coef.pval_m_race.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_m_race <- which(KIRP_coxph.coef.pval_m_race.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_m_race.df[w_KIRP_cox_m_race,])\n",
    "                                                                  \n",
    "KIRP_cox_m_race_diag <- lapply(KIRP_coxph.out_m_race[w_KIRP_cox_m_race], function(x) cox.zph(x))\n",
    "KIRP_cox_m_race_diag_glob_p <- unlist(lapply(KIRP_cox_m_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_m_race_diag_glob_p)\n",
    "w_KIRP_cox_m_race_diag <- which(KIRP_cox_m_race_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "major-pride",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A2  -0.7515369    0.4716412 0.0000347136\n",
      "SLC22A13 -0.6066311    0.5451845 0.0002719233\n",
      "SLC22A24 -0.6398630    0.5273647 0.0003230589\n",
      "[1] 0.002436235 0.026390115 0.022796444\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, lymph node involvement, and base factors (age, gender)\n",
    "KIRP_coxph.out_n <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + N0 + N1, \n",
    "                              data=inv.df)\n",
    "    KIRP_coxph.out_n[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_n.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_n, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_n.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_n.df) <- c(\"coef\", \"hazard_ratio\", \"pval\")\n",
    " \n",
    "                                                                                                                                                                                                               \n",
    "if(sum (KIRP_coxph.coef.pval_n.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_n <- which(KIRP_coxph.coef.pval_n.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_n.df[w_KIRP_cox_n,])\n",
    "                                                                  \n",
    "KIRP_cox_n_diag <- lapply(KIRP_coxph.out_n[w_KIRP_cox_n], function(x) cox.zph(x))\n",
    "KIRP_cox_n_diag_glob_p <- unlist(lapply(KIRP_cox_n_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_n_diag_glob_p)\n",
    "w_KIRP_cox_n_diag <- which(KIRP_cox_n_diag_glob_p < 0.05)\n",
    "}\n",
    "                                   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "18c3a1de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A2  -0.7455377    0.4744791 3.207595e-05\n",
      "SLC22A13 -0.6220821    0.5368255 1.913499e-04\n",
      "SLC22A24 -0.7126921    0.4903224 9.898186e-05\n",
      "[1] 0.03067460 0.11644770 0.09201975\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, lymph node involvement, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_n_race <- KIRP_coxph.mod_n_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- summary(coxph(Surv(time, status) ~ gene + age2 + gender + N0 + N1 + Race, data=inv.df))\n",
    "    tmp.mod <- coxph(Surv(time, status) ~ gene + age2 + gender + N0 + N1 + Race, data=inv.df)\n",
    "    KIRP_coxph.out_n_race[[i]] <- tmp.surv\n",
    "    KIRP_coxph.mod_n_race[[i]] <- tmp.mod\n",
    "}\n",
    "KIRP_coxph.coef.pval_n_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_n_race, function(x) x$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_n_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_n_race.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "                                                                                                                                                 \n",
    "if(sum(KIRP_coxph.coef.pval_n_race.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_n_race <- which(KIRP_coxph.coef.pval_n_race.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_n_race.df[w_KIRP_cox_n_race,])\n",
    "                                                                  \n",
    "KIRP_cox_n_race_diag <- lapply(KIRP_coxph.mod_n_race[w_KIRP_cox_n_race], function(x) cox.zph(x))\n",
    "KIRP_cox_n_race_diag_glob_p <- unlist(lapply(KIRP_cox_n_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_n_race_diag_glob_p)\n",
    "w_KIRP_cox_n_race_diag <- which(KIRP_cox_n_race_diag_glob_p < 0.05)\n",
    "}\n",
    "                                   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aerial-rouge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.6267465    0.5343274 1.386588e-04\n",
      "SLC22A2  -0.8081409    0.4456859 1.141854e-05\n",
      "[1] 0.02922411 0.01122410\n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, tumor size/progression, and base factors (age, gender)\n",
    "KIRP_coxph.out_t <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    tmp.surv <- coxph(Surv(time, status) ~ gene + age2 + gender + T1 + T2 + T3, \n",
    "                              data=inv.df)\n",
    "    KIRP_coxph.out_t[[i]] <- tmp.surv\n",
    "}\n",
    "KIRP_coxph.coef.pval_t.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_t, function(x) summary(x)$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_t.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_t.df) <- c(\"coef\",\"hazard_ratio\", \"pval\")\n",
    "                                                                                                                                                                                                               \n",
    "if(sum (KIRP_coxph.coef.pval_t.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_t <- which(KIRP_coxph.coef.pval_t.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_t.df[w_KIRP_cox_t,])\n",
    "                                                                  \n",
    "KIRP_cox_t_diag <- lapply(KIRP_coxph.out_t[w_KIRP_cox_t], function(x) cox.zph(x))\n",
    "KIRP_cox_t_diag_glob_p <- unlist(lapply(KIRP_cox_t_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_t_diag_glob_p)\n",
    "w_KIRP_cox_t_diag <- which(KIRP_cox_t_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "959fc0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard_ratio</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SLC22A18</th><td>-0.6503880</td><td>0.5218433</td><td>7.700767e-05</td></tr>\n",
       "\t<tr><th scope=row>SLC22A2</th><td>-0.7879744</td><td>0.4547650</td><td>1.409344e-05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & coef & hazard\\_ratio & pval\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tSLC22A18 & -0.6503880 & 0.5218433 & 7.700767e-05\\\\\n",
       "\tSLC22A2 & -0.7879744 & 0.4547650 & 1.409344e-05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 3\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard_ratio &lt;dbl&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| SLC22A18 | -0.6503880 | 0.5218433 | 7.700767e-05 |\n",
       "| SLC22A2 | -0.7879744 | 0.4547650 | 1.409344e-05 |\n",
       "\n"
      ],
      "text/plain": [
       "         coef       hazard_ratio pval        \n",
       "SLC22A18 -0.6503880 0.5218433    7.700767e-05\n",
       "SLC22A2  -0.7879744 0.4547650    1.409344e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               coef hazard_ratio         pval\n",
      "SLC22A18 -0.6503880    0.5218433 7.700767e-05\n",
      "SLC22A2  -0.7879744    0.4547650 1.409344e-05\n",
      "  SLC22A18    SLC22A2 \n",
      "0.07316868 0.04199459 \n"
     ]
    }
   ],
   "source": [
    "# Cox Proportions Hazard Model with SLC22 gene expression, tumor size/progression, race, and base factors (age, gender)\n",
    "KIRP_coxph.out_t_race <- KIRP_coxph.mod_t_race <- list()\n",
    "for (i in 1:nrow(KIRP_invNormTrans.df)) {\n",
    "    inv.df <- cbind.data.frame(KIRP_surv_inp, gene= as.numeric(KIRP_invNormTrans.df[i,]))\n",
    "    inv.df <- subset(inv.df, Race != \"indig\")\n",
    "    inv.df$Race <- droplevels(inv.df$Race)\n",
    "    tmp.surv <- summary(coxph(Surv(time, status) ~ gene + age2 + gender + T1 + T2 + T3 + Race, data=inv.df))\n",
    "    tmp.mod <- coxph(Surv(time, status) ~ gene + age2 + gender + T1 + T2 + T3 + Race, data=inv.df)\n",
    "    KIRP_coxph.out_t_race[[i]] <- tmp.surv\n",
    "    KIRP_coxph.mod_t_race[[i]] <- tmp.mod\n",
    "}\n",
    "KIRP_coxph.coef.pval_t_race.df <- do.call(rbind.data.frame, lapply(KIRP_coxph.out_t_race, function(x) x$coefficients[1,c(1,2,5)]))\n",
    "rownames(KIRP_coxph.coef.pval_t_race.df) <- EG2SYM$symbol[match(rownames(KIRP_invNormTrans.df), EG2SYM$gene_id)]\n",
    "colnames(KIRP_coxph.coef.pval_t_race.df) <- c(\"coef\", \"hazard_ratio\",\"pval\")\n",
    "KIRP_coxph.coef.pval_t_race.df[KIRP_coxph.coef.pval_t_race.df$pval < 0.0005,]\n",
    "names(KIRP_coxph.mod_t_race) <- names(KIRP_coxph.out_t_race) <- rownames(KIRP_coxph.coef.pval_t_race.df)\n",
    "                                                                                                                                                                                                                    \n",
    "if(sum (KIRP_coxph.coef.pval_t_race.df$pval < 0.0005) > 0) {\n",
    "w_KIRP_cox_t_race <- which(KIRP_coxph.coef.pval_t_race.df$pval < 0.0005)\n",
    "show(KIRP_coxph.coef.pval_t_race.df[w_KIRP_cox_t_race,])\n",
    "                                                                  \n",
    "KIRP_cox_t_race_diag <- lapply(KIRP_coxph.mod_t_race[w_KIRP_cox_t_race], function(x) cox.zph(x))\n",
    "KIRP_cox_t_race_diag_glob_p <- unlist(lapply(KIRP_cox_t_race_diag, function(x) {\n",
    "    as.data.frame(x$table)$p[nrow(as.data.frame(x$table))]\n",
    "})) \n",
    "show(KIRP_cox_t_race_diag_glob_p)\n",
    "w_KIRP_cox_t_race_diag <- which(KIRP_cox_t_race_diag_glob_p < 0.05)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "municipal-password",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$SLC22A18</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.5529791</td><td>0.5752336</td><td>4.015022e-04</td><td>base          </td><td>3.396312</td><td>0.0014052576</td><td>2.852244</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.4555792</td><td>0.6340806</td><td>5.443624e-03</td><td>race          </td><td>2.264112</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.4597203</td><td>0.6314602</td><td>5.643067e-03</td><td>priMalig      </td><td>2.248485</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.4671379</td><td>0.6267937</td><td>4.542739e-03</td><td>priMalig_race </td><td>2.342682</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.4485935</td><td>0.6385256</td><td>6.722625e-03</td><td>treatment     </td><td>2.172461</td><td>0.0078430627</td><td>2.105514</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.4551692</td><td>0.6343406</td><td>5.563081e-03</td><td>treatment_race</td><td>2.254685</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.5224717</td><td>0.5930529</td><td>5.201296e-04</td><td>stage         </td><td>3.283888</td><td>0.0014563628</td><td>2.836730</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.5370928</td><td>0.5844449</td><td>3.296065e-04</td><td>stage_race    </td><td>3.482004</td><td>0.0014052576</td><td>2.852244</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.6267465</td><td>0.5343274</td><td>1.386588e-04</td><td>t             </td><td>3.858053</td><td>0.0009706116</td><td>3.012955</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.6503880</td><td>0.5218433</td><td>7.700767e-05</td><td>t_race        </td><td>4.113466</td><td>0.0009706116</td><td>3.012955</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.4233537</td><td>0.6548469</td><td>4.340832e-03</td><td>n             </td><td>2.362427</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.4245808</td><td>0.6540439</td><td>4.014569e-03</td><td>n_race        </td><td>2.396361</td><td>0.0071820852</td><td>2.143749</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.4336793</td><td>0.6481201</td><td>9.557998e-03</td><td>m             </td><td>2.019633</td><td>0.0095579976</td><td>2.019633</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.4434774</td><td>0.6418007</td><td>8.342631e-03</td><td>m_race        </td><td>2.078697</td><td>0.0089843718</td><td>2.046512</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A1</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>0.13474132</td><td>1.144241</td><td>0.3288761</td><td>base          </td><td>0.48296774</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>0.10537154</td><td>1.111123</td><td>0.4804395</td><td>race          </td><td>0.31836134</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>0.10310895</td><td>1.108612</td><td>0.4824802</td><td>priMalig      </td><td>0.31652055</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>0.10249956</td><td>1.107937</td><td>0.4926811</td><td>priMalig_race </td><td>0.30743406</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>0.11160181</td><td>1.118068</td><td>0.4493267</td><td>treatment     </td><td>0.34743780</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>0.11150145</td><td>1.117955</td><td>0.4579416</td><td>treatment_race</td><td>0.33918989</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>0.25554519</td><td>1.291165</td><td>0.1033279</td><td>stage         </td><td>0.98578258</td><td>0.7444004</td><td>0.12819340</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>0.25689877</td><td>1.292914</td><td>0.1063429</td><td>stage_race    </td><td>0.97329144</td><td>0.7444004</td><td>0.12819340</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>0.00388421</td><td>1.003892</td><td>0.9769607</td><td>t             </td><td>0.01012292</td><td>0.9769607</td><td>0.01012292</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>0.01717292</td><td>1.017321</td><td>0.9004880</td><td>t_race        </td><td>0.04552209</td><td>0.9697563</td><td>0.01333740</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>0.08478981</td><td>1.088488</td><td>0.5507965</td><td>n             </td><td>0.25900882</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>0.07472680</td><td>1.077590</td><td>0.6070861</td><td>n_race        </td><td>0.21674969</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>0.07591994</td><td>1.078876</td><td>0.6171934</td><td>m             </td><td>0.20957870</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>0.06555997</td><td>1.067757</td><td>0.6703707</td><td>m_race        </td><td>0.17368499</td><td>0.7820991</td><td>0.10673820</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A3</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.1576155</td><td>0.8541781</td><td>0.38488802</td><td>base          </td><td>0.4146656</td><td>0.44903602</td><td>0.3477188</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.3487025</td><td>0.7056030</td><td>0.02978642</td><td>race          </td><td>1.5259816</td><td>0.08782176</td><td>1.0563979</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.3618447</td><td>0.6963905</td><td>0.02634301</td><td>priMalig      </td><td>1.5793345</td><td>0.08782176</td><td>1.0563979</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.3480427</td><td>0.7060687</td><td>0.03136491</td><td>priMalig_race </td><td>1.5035559</td><td>0.08782176</td><td>1.0563979</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3720835</td><td>0.6892967</td><td>0.02353388</td><td>treatment     </td><td>1.6283064</td><td>0.08782176</td><td>1.0563979</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.3585744</td><td>0.6986716</td><td>0.02775584</td><td>treatment_race</td><td>1.5566456</td><td>0.08782176</td><td>1.0563979</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.1356536</td><td>0.8731450</td><td>0.46334024</td><td>stage         </td><td>0.3341000</td><td>0.46334024</td><td>0.3341000</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.1379468</td><td>0.8711451</td><td>0.45206244</td><td>stage_race    </td><td>0.3448016</td><td>0.46334024</td><td>0.3341000</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.2888458</td><td>0.7491277</td><td>0.09247548</td><td>t             </td><td>1.0339734</td><td>0.16183209</td><td>0.7909354</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.2758034</td><td>0.7589621</td><td>0.11050628</td><td>t_race        </td><td>0.9566130</td><td>0.17189866</td><td>0.7647275</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.1865966</td><td>0.8297784</td><td>0.28685219</td><td>n             </td><td>0.5423418</td><td>0.40159307</td><td>0.3962138</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.1714353</td><td>0.8424548</td><td>0.32561348</td><td>n_race        </td><td>0.4872976</td><td>0.41441715</td><td>0.3825623</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.3065829</td><td>0.7359575</td><td>0.07736576</td><td>m             </td><td>1.1114512</td><td>0.16183209</td><td>0.7909354</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.2903929</td><td>0.7479696</td><td>0.08996423</td><td>m_race        </td><td>1.0459301</td><td>0.16183209</td><td>0.7909354</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A2</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.7817740</td><td>0.4575935</td><td>3.550625e-05</td><td>base          </td><td>4.449695</td><td>3.550625e-05</td><td>4.449695</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.8852318</td><td>0.4126185</td><td>2.829581e-06</td><td>race          </td><td>5.548278</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.9105052</td><td>0.4023209</td><td>1.096492e-06</td><td>priMalig      </td><td>5.959994</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.8863224</td><td>0.4121688</td><td>2.294701e-06</td><td>priMalig_race </td><td>5.639274</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.9087330</td><td>0.4030346</td><td>1.376105e-06</td><td>treatment     </td><td>5.861348</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.8841183</td><td>0.4130782</td><td>2.876815e-06</td><td>treatment_race</td><td>5.541088</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.8608859</td><td>0.4227874</td><td>1.695237e-06</td><td>stage         </td><td>5.770770</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.8513621</td><td>0.4268331</td><td>1.434635e-06</td><td>stage_race    </td><td>5.843259</td><td>5.753629e-06</td><td>5.240058</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.8081409</td><td>0.4456859</td><td>1.141854e-05</td><td>t             </td><td>4.942389</td><td>1.818226e-05</td><td>4.740352</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.7879744</td><td>0.4547650</td><td>1.409344e-05</td><td>t_race        </td><td>4.850983</td><td>1.973082e-05</td><td>4.704855</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.7515369</td><td>0.4716412</td><td>3.471360e-05</td><td>n             </td><td>4.459500</td><td>3.550625e-05</td><td>4.449695</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.7455377</td><td>0.4744791</td><td>3.207595e-05</td><td>n_race        </td><td>4.493820</td><td>3.550625e-05</td><td>4.449695</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.8844331</td><td>0.4129482</td><td>1.168860e-05</td><td>m             </td><td>4.932238</td><td>1.818226e-05</td><td>4.740352</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.8445918</td><td>0.4297328</td><td>2.523022e-05</td><td>m_race        </td><td>4.598079</td><td>3.211119e-05</td><td>4.493344</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A4</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.2961243</td><td>0.7436950</td><td>0.08900649</td><td>base          </td><td>1.0505783</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.2962588</td><td>0.7435950</td><td>0.10163499</td><td>race          </td><td>0.9929568</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.3040131</td><td>0.7378512</td><td>0.08917201</td><td>priMalig      </td><td>1.0497715</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.2933213</td><td>0.7457825</td><td>0.10739933</td><td>priMalig_race </td><td>0.9689984</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3168036</td><td>0.7284738</td><td>0.07605500</td><td>treatment     </td><td>1.1188722</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.3057072</td><td>0.7366022</td><td>0.09262746</td><td>treatment_race</td><td>1.0332602</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.2788685</td><td>0.7566394</td><td>0.09631543</td><td>stage         </td><td>1.0163041</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.2777735</td><td>0.7574684</td><td>0.10214153</td><td>stage_race    </td><td>0.9907977</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.2870936</td><td>0.7504414</td><td>0.09764978</td><td>t             </td><td>1.0103287</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.2771767</td><td>0.7579206</td><td>0.11387763</td><td>t_race        </td><td>0.9435616</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.3557770</td><td>0.7006288</td><td>0.04814394</td><td>n             </td><td>1.3174584</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.3645341</td><td>0.6945201</td><td>0.04581471</td><td>n_race        </td><td>1.3389950</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.3292454</td><td>0.7194664</td><td>0.05893603</td><td>m             </td><td>1.2296191</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.3034125</td><td>0.7382945</td><td>0.08674550</td><td>m_race        </td><td>1.0617531</td><td>0.1138776</td><td>0.9435616</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A5</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.3214742</td><td>0.7250794</td><td>0.12147231</td><td>base          </td><td>0.9155227</td><td>0.16075520</td><td>0.7938350</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.4119284</td><td>0.6623717</td><td>0.02639488</td><td>race          </td><td>1.5784803</td><td>0.07871222</td><td>1.1039578</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.4032794</td><td>0.6681254</td><td>0.02769500</td><td>priMalig      </td><td>1.5575987</td><td>0.07871222</td><td>1.1039578</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.4192504</td><td>0.6575396</td><td>0.02450147</td><td>priMalig_race </td><td>1.6108078</td><td>0.07871222</td><td>1.1039578</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.4018718</td><td>0.6690665</td><td>0.02811151</td><td>treatment     </td><td>1.5511159</td><td>0.07871222</td><td>1.1039578</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.4166937</td><td>0.6592228</td><td>0.02515555</td><td>treatment_race</td><td>1.5993662</td><td>0.07871222</td><td>1.1039578</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.2203511</td><td>0.8022371</td><td>0.28074113</td><td>stage         </td><td>0.5516939</td><td>0.28074113</td><td>0.5516939</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.2294049</td><td>0.7950065</td><td>0.26790881</td><td>stage_race    </td><td>0.5720130</td><td>0.28074113</td><td>0.5516939</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.3050243</td><td>0.7371055</td><td>0.13728672</td><td>t             </td><td>0.8623715</td><td>0.16075520</td><td>0.7938350</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.3078603</td><td>0.7350180</td><td>0.13779017</td><td>t_race        </td><td>0.8607818</td><td>0.16075520</td><td>0.7938350</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.3794710</td><td>0.6842233</td><td>0.05398372</td><td>n             </td><td>1.2677372</td><td>0.08776018</td><td>1.0567025</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.3943869</td><td>0.6740932</td><td>0.04511138</td><td>n_race        </td><td>1.3457139</td><td>0.08776018</td><td>1.0567025</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.3447614</td><td>0.7083894</td><td>0.04984492</td><td>m             </td><td>1.3023791</td><td>0.08776018</td><td>1.0567025</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.3393600</td><td>0.7122260</td><td>0.05641726</td><td>m_race        </td><td>1.2485880</td><td>0.08776018</td><td>1.0567025</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A6</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.11754768</td><td>0.8890981</td><td>0.5191883</td><td>base          </td><td>0.28467513</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.04552751</td><td>0.9554933</td><td>0.7938364</td><td>race          </td><td>0.10026898</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.04464710</td><td>0.9563349</td><td>0.7949461</td><td>priMalig      </td><td>0.09966232</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.04448794</td><td>0.9564871</td><td>0.7976486</td><td>priMalig_race </td><td>0.09818841</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.04835582</td><td>0.9527947</td><td>0.7789184</td><td>treatment     </td><td>0.10850805</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.04722964</td><td>0.9538683</td><td>0.7863848</td><td>treatment_race</td><td>0.10436486</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.04675201</td><td>0.9543240</td><td>0.8019877</td><td>stage         </td><td>0.09583231</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.05053270</td><td>0.9507228</td><td>0.7868357</td><td>stage_race    </td><td>0.10411595</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.21170254</td><td>0.8092054</td><td>0.2615762</td><td>t             </td><td>0.58240184</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.21568404</td><td>0.8059899</td><td>0.2536680</td><td>t_race        </td><td>0.59573423</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.15067649</td><td>0.8601259</td><td>0.3909433</td><td>n             </td><td>0.40788624</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.16811617</td><td>0.8452556</td><td>0.3442363</td><td>n_race        </td><td>0.46314330</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.15625882</td><td>0.8553378</td><td>0.3884121</td><td>m             </td><td>0.41070730</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.15169123</td><td>0.8592536</td><td>0.4066059</td><td>m_race        </td><td>0.39082635</td><td>0.8019877</td><td>0.09583231</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A8</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.08466804</td><td>0.9188172</td><td>0.6434806</td><td>base          </td><td>0.19146457</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>race</th><td> 0.09806979</td><td>1.1030398</td><td>0.5506169</td><td>race          </td><td>0.25915045</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td> 0.10093490</td><td>1.1062046</td><td>0.5367857</td><td>priMalig      </td><td>0.27019905</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td> 0.09602066</td><td>1.1007818</td><td>0.5586265</td><td>priMalig_race </td><td>0.25287845</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td> 0.10100838</td><td>1.1062859</td><td>0.5357554</td><td>treatment     </td><td>0.27103348</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td> 0.09713791</td><td>1.1020123</td><td>0.5538041</td><td>treatment_race</td><td>0.25664386</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.01958032</td><td>0.9806101</td><td>0.9152165</td><td>stage         </td><td>0.03847617</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.02761548</td><td>0.9727623</td><td>0.8801478</td><td>stage_race    </td><td>0.05544441</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.03636214</td><td>0.9642910</td><td>0.8462808</td><td>t             </td><td>0.07248551</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.04350308</td><td>0.9574296</td><td>0.8170881</td><td>t_race        </td><td>0.08773110</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.01612031</td><td>0.9840089</td><td>0.9240376</td><td>n             </td><td>0.03431035</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.03273859</td><td>0.9677915</td><td>0.8491155</td><td>n_race        </td><td>0.07103325</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.06656792</td><td>0.9355994</td><td>0.7062399</td><td>m             </td><td>0.15104774</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.08260712</td><td>0.9207128</td><td>0.6461885</td><td>m_race        </td><td>0.18964080</td><td>0.9240376</td><td>0.03431035</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A14</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.11599982</td><td>0.8904754</td><td>0.49459540</td><td>base          </td><td>0.3057499</td><td>0.5770280</td><td>0.2388031</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.23182824</td><td>0.7930823</td><td>0.16319892</td><td>race          </td><td>0.7872827</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.22081631</td><td>0.8018640</td><td>0.18198340</td><td>priMalig      </td><td>0.7399682</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.23473619</td><td>0.7907794</td><td>0.15651092</td><td>priMalig_race </td><td>0.8054554</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.21734938</td><td>0.8046488</td><td>0.19215218</td><td>treatment     </td><td>0.7163547</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.23094084</td><td>0.7937864</td><td>0.16605555</td><td>treatment_race</td><td>0.7797466</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.05644030</td><td>0.9451229</td><td>0.72883238</td><td>stage         </td><td>0.1373723</td><td>0.7288324</td><td>0.1373723</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.06691126</td><td>0.9352782</td><td>0.68624395</td><td>stage_race    </td><td>0.1635215</td><td>0.7288324</td><td>0.1373723</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.19703065</td><td>0.8211655</td><td>0.21827773</td><td>t             </td><td>0.6609906</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.19785233</td><td>0.8204910</td><td>0.22217356</td><td>t_race        </td><td>0.6533076</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.30141010</td><td>0.7397743</td><td>0.07757237</td><td>n             </td><td>1.1102929</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.28448587</td><td>0.7524010</td><td>0.09836765</td><td>n_race        </td><td>1.0071477</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.29020266</td><td>0.7481119</td><td>0.08973468</td><td>m             </td><td>1.0470397</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.31136372</td><td>0.7324474</td><td>0.06869615</td><td>m_race        </td><td>1.1630676</td><td>0.2827663</td><td>0.5485723</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A13</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.3435566</td><td>0.7092433</td><td>0.0268222290</td><td>base          </td><td>1.571505</td><td>0.031292600</td><td>1.504558</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.4244354</td><td>0.6541390</td><td>0.0078498277</td><td>race          </td><td>2.105140</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.4003243</td><td>0.6701027</td><td>0.0117622811</td><td>priMalig      </td><td>1.929508</td><td>0.015556029</td><td>1.808101</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.4317372</td><td>0.6493800</td><td>0.0069765180</td><td>priMalig_race </td><td>2.156361</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3997782</td><td>0.6704687</td><td>0.0122225943</td><td>treatment     </td><td>1.912837</td><td>0.015556029</td><td>1.808101</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.4282103</td><td>0.6516744</td><td>0.0076029911</td><td>treatment_race</td><td>2.119016</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.2792254</td><td>0.7563694</td><td>0.0795272728</td><td>stage         </td><td>1.099484</td><td>0.079527273</td><td>1.099484</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.3282254</td><td>0.7202007</td><td>0.0420689445</td><td>stage_race    </td><td>1.376038</td><td>0.045305017</td><td>1.343854</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.3998200</td><td>0.6704407</td><td>0.0081350474</td><td>t             </td><td>2.089640</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.4324787</td><td>0.6488987</td><td>0.0047037037</td><td>t_race        </td><td>2.327560</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.6066311</td><td>0.5451845</td><td>0.0002719233</td><td>n             </td><td>3.565554</td><td>0.001903463</td><td>2.720456</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.6220821</td><td>0.5368255</td><td>0.0001913499</td><td>n_race        </td><td>3.718172</td><td>0.001903463</td><td>2.720456</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.4396106</td><td>0.6442873</td><td>0.0063089152</td><td>m             </td><td>2.200045</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.4638319</td><td>0.6288693</td><td>0.0043320615</td><td>m_race        </td><td>2.363305</td><td>0.012654518</td><td>1.897754</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A7</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.3106495</td><td>0.7329708</td><td>0.09701397</td><td>base          </td><td>1.0131657</td><td>0.1269405</td><td>0.8963998</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.3460993</td><td>0.7074423</td><td>0.04675172</td><td>race          </td><td>1.3302024</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.3158003</td><td>0.7292050</td><td>0.06267052</td><td>priMalig      </td><td>1.2029367</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.3451365</td><td>0.7081237</td><td>0.04743595</td><td>priMalig_race </td><td>1.3238924</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3175991</td><td>0.7278946</td><td>0.06052995</td><td>treatment     </td><td>1.2180297</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.3480718</td><td>0.7060482</td><td>0.04554466</td><td>treatment_race</td><td>1.3415625</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.2092262</td><td>0.8112117</td><td>0.27034234</td><td>stage         </td><td>0.5680859</td><td>0.2703423</td><td>0.5680859</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.2551871</td><td>0.7747715</td><td>0.19359950</td><td>stage_race    </td><td>0.7130958</td><td>0.2084918</td><td>0.6809111</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.2994011</td><td>0.7412621</td><td>0.11458166</td><td>t             </td><td>0.9408849</td><td>0.1336786</td><td>0.8739381</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.3196238</td><td>0.7264222</td><td>0.09973896</td><td>t_race        </td><td>1.0011352</td><td>0.1269405</td><td>0.8963998</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.4011960</td><td>0.6695188</td><td>0.02613977</td><td>n             </td><td>1.5826982</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.4223577</td><td>0.6554995</td><td>0.02070923</td><td>n_race        </td><td>1.6838360</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.3012729</td><td>0.7398759</td><td>0.08302095</td><td>m             </td><td>1.0808123</td><td>0.1269405</td><td>0.8963998</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.3292805</td><td>0.7194412</td><td>0.06255784</td><td>m_race        </td><td>1.2037182</td><td>0.1096734</td><td>0.9598987</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A17</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.1349697</td><td>0.8737424</td><td>0.35969783</td><td>base          </td><td>0.4440622</td><td>0.3596978</td><td>0.4440622</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.2726086</td><td>0.7613907</td><td>0.06952133</td><td>race          </td><td>1.1578819</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.2474031</td><td>0.7808259</td><td>0.09642004</td><td>priMalig      </td><td>1.0158327</td><td>0.1279569</td><td>0.8929364</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.2800633</td><td>0.7557359</td><td>0.06225612</td><td>priMalig_race </td><td>1.2058179</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.2464894</td><td>0.7815396</td><td>0.10053753</td><td>treatment     </td><td>0.9976718</td><td>0.1279569</td><td>0.8929364</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.2757088</td><td>0.7590339</td><td>0.06802739</td><td>treatment_race</td><td>1.1673162</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.2015116</td><td>0.8174941</td><td>0.16882792</td><td>stage         </td><td>0.7725557</td><td>0.1818147</td><td>0.7403711</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.2582645</td><td>0.7723909</td><td>0.08204171</td><td>stage_race    </td><td>1.0859653</td><td>0.1276204</td><td>0.8940798</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.2253631</td><td>0.7982263</td><td>0.11036446</td><td>t             </td><td>0.9571708</td><td>0.1287585</td><td>0.8902240</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.2791573</td><td>0.7564209</td><td>0.05465295</td><td>t_race        </td><td>1.2623864</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.2929777</td><td>0.7460388</td><td>0.02961009</td><td>n             </td><td>1.5285603</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.3053085</td><td>0.7368960</td><td>0.03144463</td><td>n_race        </td><td>1.5024535</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.2778825</td><td>0.7573858</td><td>0.05459017</td><td>m             </td><td>1.2628856</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.3028784</td><td>0.7386889</td><td>0.04792513</td><td>m_race        </td><td>1.3194367</td><td>0.1216623</td><td>0.9148438</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A15</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>0.2688539</td><td>1.308464</td><td>0.07434015</td><td>base          </td><td>1.1287765</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>0.2128963</td><td>1.237256</td><td>0.17151090</td><td>race          </td><td>0.7657083</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>0.2119131</td><td>1.236040</td><td>0.16761025</td><td>priMalig      </td><td>0.7756994</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>0.2110181</td><td>1.234935</td><td>0.17494071</td><td>priMalig_race </td><td>0.7571091</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>0.2242117</td><td>1.251336</td><td>0.14627079</td><td>treatment     </td><td>0.8348424</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>0.2231841</td><td>1.250051</td><td>0.15574360</td><td>treatment_race</td><td>0.8075898</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>0.4197153</td><td>1.521528</td><td>0.00881893</td><td>stage         </td><td>2.0545841</td><td>0.06173251</td><td>1.2094861</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>0.4298565</td><td>1.537037</td><td>0.00791663</td><td>stage_race    </td><td>2.1014596</td><td>0.06173251</td><td>1.2094861</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>0.1861073</td><td>1.204551</td><td>0.21018728</td><td>t             </td><td>0.6773936</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>0.1975572</td><td>1.218423</td><td>0.18670321</td><td>t_race        </td><td>0.7288482</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>0.2039938</td><td>1.226291</td><td>0.20274418</td><td>n             </td><td>0.6930516</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>0.2381646</td><td>1.268918</td><td>0.14517008</td><td>n_race        </td><td>0.8381229</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>0.2343360</td><td>1.264069</td><td>0.14160843</td><td>m             </td><td>0.8489109</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>0.2446312</td><td>1.277150</td><td>0.13075190</td><td>m_race        </td><td>0.8835520</td><td>0.21018728</td><td>0.6773936</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A11</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.3635193</td><td>0.6952253</td><td>0.03206192</td><td>base          </td><td>1.494010</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.3119401</td><td>0.7320254</td><td>0.04698779</td><td>race          </td><td>1.328015</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.3122723</td><td>0.7317823</td><td>0.04485897</td><td>priMalig      </td><td>1.348151</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.3180000</td><td>0.7276028</td><td>0.04321942</td><td>priMalig_race </td><td>1.364321</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3084579</td><td>0.7345788</td><td>0.04691170</td><td>treatment     </td><td>1.328719</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.3136502</td><td>0.7307746</td><td>0.04603630</td><td>treatment_race</td><td>1.336900</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.3936648</td><td>0.6745801</td><td>0.02177259</td><td>stage         </td><td>1.662090</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.4057206</td><td>0.6664963</td><td>0.01657187</td><td>stage_race    </td><td>1.780629</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.3516416</td><td>0.7035322</td><td>0.04378668</td><td>t             </td><td>1.358658</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.3623216</td><td>0.6960585</td><td>0.03486353</td><td>t_race        </td><td>1.457629</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.3941567</td><td>0.6742484</td><td>0.01178682</td><td>n             </td><td>1.928603</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.4026736</td><td>0.6685303</td><td>0.01106625</td><td>n_race        </td><td>1.956000</td><td>0.05481908</td><td>1.261068</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.2850493</td><td>0.7519771</td><td>0.06356282</td><td>m             </td><td>1.196797</td><td>0.06845226</td><td>1.164612</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.2825700</td><td>0.7538438</td><td>0.06850734</td><td>m_race        </td><td>1.164263</td><td>0.06850734</td><td>1.164263</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A23</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>0.4306382</td><td>1.538239</td><td>0.014916256</td><td>base          </td><td>1.826340</td><td>0.02610345</td><td>1.583302</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>0.4202414</td><td>1.522329</td><td>0.010141267</td><td>race          </td><td>1.993908</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>0.4154642</td><td>1.515074</td><td>0.010298946</td><td>priMalig      </td><td>1.987207</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>0.4219007</td><td>1.524857</td><td>0.009939883</td><td>priMalig_race </td><td>2.002619</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>0.4147639</td><td>1.514013</td><td>0.010234579</td><td>treatment     </td><td>1.989930</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>0.4222213</td><td>1.525346</td><td>0.009751555</td><td>treatment_race</td><td>2.010926</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>0.4879161</td><td>1.628918</td><td>0.003616781</td><td>stage         </td><td>2.441678</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>0.5033201</td><td>1.654204</td><td>0.002904347</td><td>stage_race    </td><td>2.536951</td><td>0.02059789</td><td>1.686177</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>0.3296883</td><td>1.390535</td><td>0.062993722</td><td>t             </td><td>1.200703</td><td>0.06849331</td><td>1.164352</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>0.3251847</td><td>1.384286</td><td>0.068938410</td><td>t_race        </td><td>1.161539</td><td>0.06893841</td><td>1.161539</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>0.3456039</td><td>1.412843</td><td>0.057023115</td><td>n             </td><td>1.243949</td><td>0.06849331</td><td>1.164352</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>0.3424405</td><td>1.408381</td><td>0.063600927</td><td>n_race        </td><td>1.196537</td><td>0.06849331</td><td>1.164352</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>0.3571492</td><td>1.429249</td><td>0.042185009</td><td>m             </td><td>1.374842</td><td>0.05905901</td><td>1.228714</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>0.3671640</td><td>1.443635</td><td>0.037741551</td><td>m_race        </td><td>1.423180</td><td>0.05870908</td><td>1.231295</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A12</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.4526312</td><td>0.6359526</td><td>0.010797048</td><td>base          </td><td>1.966695</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.3617442</td><td>0.6964605</td><td>0.024944324</td><td>race          </td><td>1.603028</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.3486193</td><td>0.7056617</td><td>0.025974714</td><td>priMalig      </td><td>1.585449</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.3677997</td><td>0.6922558</td><td>0.022361922</td><td>priMalig_race </td><td>1.650491</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.3441197</td><td>0.7088441</td><td>0.028489127</td><td>treatment     </td><td>1.545321</td><td>0.02848913</td><td>1.545321</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.3612062</td><td>0.6968353</td><td>0.025152561</td><td>treatment_race</td><td>1.599418</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.3945856</td><td>0.6739593</td><td>0.024839065</td><td>stage         </td><td>1.604865</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.4353729</td><td>0.6470234</td><td>0.015185631</td><td>stage_race    </td><td>1.818567</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.4476685</td><td>0.6391165</td><td>0.014061958</td><td>t             </td><td>1.851954</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.4686108</td><td>0.6258712</td><td>0.011283793</td><td>t_race        </td><td>1.947545</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.4513201</td><td>0.6367870</td><td>0.004655780</td><td>n             </td><td>2.332008</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.4526342</td><td>0.6359507</td><td>0.005398379</td><td>n_race        </td><td>2.267737</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.3847837</td><td>0.6805978</td><td>0.016652670</td><td>m             </td><td>1.778516</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.3973236</td><td>0.6721165</td><td>0.015958008</td><td>m_race        </td><td>1.797021</td><td>0.02797277</td><td>1.553265</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SLC22A24</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coef</th><th scope=col>hazard</th><th scope=col>pval</th><th scope=col>comp</th><th scope=col>negLog.pval</th><th scope=col>padj</th><th scope=col>negLog.padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>base</th><td>-0.6141615</td><td>0.5410944</td><td>7.279835e-04</td><td>base          </td><td>3.137878</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>race</th><td>-0.6056222</td><td>0.5457347</td><td>5.743739e-04</td><td>race          </td><td>3.240805</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>priMalig</th><td>-0.5916532</td><td>0.5534116</td><td>6.834294e-04</td><td>priMalig      </td><td>3.165306</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>priMalig_race</th><td>-0.6179197</td><td>0.5390647</td><td>5.080191e-04</td><td>priMalig_race </td><td>3.294120</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>treatment</th><td>-0.5801044</td><td>0.5598399</td><td>7.394708e-04</td><td>treatment     </td><td>3.131079</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>treatment_race</th><td>-0.6077782</td><td>0.5445594</td><td>5.399233e-04</td><td>treatment_race</td><td>3.267668</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>stage</th><td>-0.7178532</td><td>0.4877984</td><td>4.989184e-05</td><td>stage         </td><td>4.301970</td><td>0.0003492429</td><td>3.456872</td></tr>\n",
       "\t<tr><th scope=row>stage_race</th><td>-0.7573798</td><td>0.4688934</td><td>3.263103e-05</td><td>stage_race    </td><td>4.486369</td><td>0.0003492429</td><td>3.456872</td></tr>\n",
       "\t<tr><th scope=row>t</th><td>-0.5256927</td><td>0.5911457</td><td>3.794892e-03</td><td>t             </td><td>2.420801</td><td>0.0037948915</td><td>2.420801</td></tr>\n",
       "\t<tr><th scope=row>t_race</th><td>-0.5413052</td><td>0.5819881</td><td>3.251946e-03</td><td>t_race        </td><td>2.487857</td><td>0.0035020960</td><td>2.455672</td></tr>\n",
       "\t<tr><th scope=row>n</th><td>-0.6398630</td><td>0.5273647</td><td>3.230589e-04</td><td>n             </td><td>3.490718</td><td>0.0010352591</td><td>2.984951</td></tr>\n",
       "\t<tr><th scope=row>n_race</th><td>-0.7126921</td><td>0.4903224</td><td>9.898186e-05</td><td>n_race        </td><td>4.004444</td><td>0.0004619153</td><td>3.335438</td></tr>\n",
       "\t<tr><th scope=row>m</th><td>-0.5309554</td><td>0.5880429</td><td>1.925392e-03</td><td>m             </td><td>2.715481</td><td>0.0022462908</td><td>2.648534</td></tr>\n",
       "\t<tr><th scope=row>m_race</th><td>-0.5649519</td><td>0.5683875</td><td>1.263062e-03</td><td>m_race        </td><td>2.898575</td><td>0.0016075339</td><td>2.793840</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$SLC22A18] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.5529791 & 0.5752336 & 4.015022e-04 & base           & 3.396312 & 0.0014052576 & 2.852244\\\\\n",
       "\trace & -0.4555792 & 0.6340806 & 5.443624e-03 & race           & 2.264112 & 0.0071820852 & 2.143749\\\\\n",
       "\tpriMalig & -0.4597203 & 0.6314602 & 5.643067e-03 & priMalig       & 2.248485 & 0.0071820852 & 2.143749\\\\\n",
       "\tpriMalig\\_race & -0.4671379 & 0.6267937 & 4.542739e-03 & priMalig\\_race  & 2.342682 & 0.0071820852 & 2.143749\\\\\n",
       "\ttreatment & -0.4485935 & 0.6385256 & 6.722625e-03 & treatment      & 2.172461 & 0.0078430627 & 2.105514\\\\\n",
       "\ttreatment\\_race & -0.4551692 & 0.6343406 & 5.563081e-03 & treatment\\_race & 2.254685 & 0.0071820852 & 2.143749\\\\\n",
       "\tstage & -0.5224717 & 0.5930529 & 5.201296e-04 & stage          & 3.283888 & 0.0014563628 & 2.836730\\\\\n",
       "\tstage\\_race & -0.5370928 & 0.5844449 & 3.296065e-04 & stage\\_race     & 3.482004 & 0.0014052576 & 2.852244\\\\\n",
       "\tt & -0.6267465 & 0.5343274 & 1.386588e-04 & t              & 3.858053 & 0.0009706116 & 3.012955\\\\\n",
       "\tt\\_race & -0.6503880 & 0.5218433 & 7.700767e-05 & t\\_race         & 4.113466 & 0.0009706116 & 3.012955\\\\\n",
       "\tn & -0.4233537 & 0.6548469 & 4.340832e-03 & n              & 2.362427 & 0.0071820852 & 2.143749\\\\\n",
       "\tn\\_race & -0.4245808 & 0.6540439 & 4.014569e-03 & n\\_race         & 2.396361 & 0.0071820852 & 2.143749\\\\\n",
       "\tm & -0.4336793 & 0.6481201 & 9.557998e-03 & m              & 2.019633 & 0.0095579976 & 2.019633\\\\\n",
       "\tm\\_race & -0.4434774 & 0.6418007 & 8.342631e-03 & m\\_race         & 2.078697 & 0.0089843718 & 2.046512\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A1] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & 0.13474132 & 1.144241 & 0.3288761 & base           & 0.48296774 & 0.7820991 & 0.10673820\\\\\n",
       "\trace & 0.10537154 & 1.111123 & 0.4804395 & race           & 0.31836134 & 0.7820991 & 0.10673820\\\\\n",
       "\tpriMalig & 0.10310895 & 1.108612 & 0.4824802 & priMalig       & 0.31652055 & 0.7820991 & 0.10673820\\\\\n",
       "\tpriMalig\\_race & 0.10249956 & 1.107937 & 0.4926811 & priMalig\\_race  & 0.30743406 & 0.7820991 & 0.10673820\\\\\n",
       "\ttreatment & 0.11160181 & 1.118068 & 0.4493267 & treatment      & 0.34743780 & 0.7820991 & 0.10673820\\\\\n",
       "\ttreatment\\_race & 0.11150145 & 1.117955 & 0.4579416 & treatment\\_race & 0.33918989 & 0.7820991 & 0.10673820\\\\\n",
       "\tstage & 0.25554519 & 1.291165 & 0.1033279 & stage          & 0.98578258 & 0.7444004 & 0.12819340\\\\\n",
       "\tstage\\_race & 0.25689877 & 1.292914 & 0.1063429 & stage\\_race     & 0.97329144 & 0.7444004 & 0.12819340\\\\\n",
       "\tt & 0.00388421 & 1.003892 & 0.9769607 & t              & 0.01012292 & 0.9769607 & 0.01012292\\\\\n",
       "\tt\\_race & 0.01717292 & 1.017321 & 0.9004880 & t\\_race         & 0.04552209 & 0.9697563 & 0.01333740\\\\\n",
       "\tn & 0.08478981 & 1.088488 & 0.5507965 & n              & 0.25900882 & 0.7820991 & 0.10673820\\\\\n",
       "\tn\\_race & 0.07472680 & 1.077590 & 0.6070861 & n\\_race         & 0.21674969 & 0.7820991 & 0.10673820\\\\\n",
       "\tm & 0.07591994 & 1.078876 & 0.6171934 & m              & 0.20957870 & 0.7820991 & 0.10673820\\\\\n",
       "\tm\\_race & 0.06555997 & 1.067757 & 0.6703707 & m\\_race         & 0.17368499 & 0.7820991 & 0.10673820\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A3] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.1576155 & 0.8541781 & 0.38488802 & base           & 0.4146656 & 0.44903602 & 0.3477188\\\\\n",
       "\trace & -0.3487025 & 0.7056030 & 0.02978642 & race           & 1.5259816 & 0.08782176 & 1.0563979\\\\\n",
       "\tpriMalig & -0.3618447 & 0.6963905 & 0.02634301 & priMalig       & 1.5793345 & 0.08782176 & 1.0563979\\\\\n",
       "\tpriMalig\\_race & -0.3480427 & 0.7060687 & 0.03136491 & priMalig\\_race  & 1.5035559 & 0.08782176 & 1.0563979\\\\\n",
       "\ttreatment & -0.3720835 & 0.6892967 & 0.02353388 & treatment      & 1.6283064 & 0.08782176 & 1.0563979\\\\\n",
       "\ttreatment\\_race & -0.3585744 & 0.6986716 & 0.02775584 & treatment\\_race & 1.5566456 & 0.08782176 & 1.0563979\\\\\n",
       "\tstage & -0.1356536 & 0.8731450 & 0.46334024 & stage          & 0.3341000 & 0.46334024 & 0.3341000\\\\\n",
       "\tstage\\_race & -0.1379468 & 0.8711451 & 0.45206244 & stage\\_race     & 0.3448016 & 0.46334024 & 0.3341000\\\\\n",
       "\tt & -0.2888458 & 0.7491277 & 0.09247548 & t              & 1.0339734 & 0.16183209 & 0.7909354\\\\\n",
       "\tt\\_race & -0.2758034 & 0.7589621 & 0.11050628 & t\\_race         & 0.9566130 & 0.17189866 & 0.7647275\\\\\n",
       "\tn & -0.1865966 & 0.8297784 & 0.28685219 & n              & 0.5423418 & 0.40159307 & 0.3962138\\\\\n",
       "\tn\\_race & -0.1714353 & 0.8424548 & 0.32561348 & n\\_race         & 0.4872976 & 0.41441715 & 0.3825623\\\\\n",
       "\tm & -0.3065829 & 0.7359575 & 0.07736576 & m              & 1.1114512 & 0.16183209 & 0.7909354\\\\\n",
       "\tm\\_race & -0.2903929 & 0.7479696 & 0.08996423 & m\\_race         & 1.0459301 & 0.16183209 & 0.7909354\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A2] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.7817740 & 0.4575935 & 3.550625e-05 & base           & 4.449695 & 3.550625e-05 & 4.449695\\\\\n",
       "\trace & -0.8852318 & 0.4126185 & 2.829581e-06 & race           & 5.548278 & 5.753629e-06 & 5.240058\\\\\n",
       "\tpriMalig & -0.9105052 & 0.4023209 & 1.096492e-06 & priMalig       & 5.959994 & 5.753629e-06 & 5.240058\\\\\n",
       "\tpriMalig\\_race & -0.8863224 & 0.4121688 & 2.294701e-06 & priMalig\\_race  & 5.639274 & 5.753629e-06 & 5.240058\\\\\n",
       "\ttreatment & -0.9087330 & 0.4030346 & 1.376105e-06 & treatment      & 5.861348 & 5.753629e-06 & 5.240058\\\\\n",
       "\ttreatment\\_race & -0.8841183 & 0.4130782 & 2.876815e-06 & treatment\\_race & 5.541088 & 5.753629e-06 & 5.240058\\\\\n",
       "\tstage & -0.8608859 & 0.4227874 & 1.695237e-06 & stage          & 5.770770 & 5.753629e-06 & 5.240058\\\\\n",
       "\tstage\\_race & -0.8513621 & 0.4268331 & 1.434635e-06 & stage\\_race     & 5.843259 & 5.753629e-06 & 5.240058\\\\\n",
       "\tt & -0.8081409 & 0.4456859 & 1.141854e-05 & t              & 4.942389 & 1.818226e-05 & 4.740352\\\\\n",
       "\tt\\_race & -0.7879744 & 0.4547650 & 1.409344e-05 & t\\_race         & 4.850983 & 1.973082e-05 & 4.704855\\\\\n",
       "\tn & -0.7515369 & 0.4716412 & 3.471360e-05 & n              & 4.459500 & 3.550625e-05 & 4.449695\\\\\n",
       "\tn\\_race & -0.7455377 & 0.4744791 & 3.207595e-05 & n\\_race         & 4.493820 & 3.550625e-05 & 4.449695\\\\\n",
       "\tm & -0.8844331 & 0.4129482 & 1.168860e-05 & m              & 4.932238 & 1.818226e-05 & 4.740352\\\\\n",
       "\tm\\_race & -0.8445918 & 0.4297328 & 2.523022e-05 & m\\_race         & 4.598079 & 3.211119e-05 & 4.493344\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A4] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.2961243 & 0.7436950 & 0.08900649 & base           & 1.0505783 & 0.1138776 & 0.9435616\\\\\n",
       "\trace & -0.2962588 & 0.7435950 & 0.10163499 & race           & 0.9929568 & 0.1138776 & 0.9435616\\\\\n",
       "\tpriMalig & -0.3040131 & 0.7378512 & 0.08917201 & priMalig       & 1.0497715 & 0.1138776 & 0.9435616\\\\\n",
       "\tpriMalig\\_race & -0.2933213 & 0.7457825 & 0.10739933 & priMalig\\_race  & 0.9689984 & 0.1138776 & 0.9435616\\\\\n",
       "\ttreatment & -0.3168036 & 0.7284738 & 0.07605500 & treatment      & 1.1188722 & 0.1138776 & 0.9435616\\\\\n",
       "\ttreatment\\_race & -0.3057072 & 0.7366022 & 0.09262746 & treatment\\_race & 1.0332602 & 0.1138776 & 0.9435616\\\\\n",
       "\tstage & -0.2788685 & 0.7566394 & 0.09631543 & stage          & 1.0163041 & 0.1138776 & 0.9435616\\\\\n",
       "\tstage\\_race & -0.2777735 & 0.7574684 & 0.10214153 & stage\\_race     & 0.9907977 & 0.1138776 & 0.9435616\\\\\n",
       "\tt & -0.2870936 & 0.7504414 & 0.09764978 & t              & 1.0103287 & 0.1138776 & 0.9435616\\\\\n",
       "\tt\\_race & -0.2771767 & 0.7579206 & 0.11387763 & t\\_race         & 0.9435616 & 0.1138776 & 0.9435616\\\\\n",
       "\tn & -0.3557770 & 0.7006288 & 0.04814394 & n              & 1.3174584 & 0.1138776 & 0.9435616\\\\\n",
       "\tn\\_race & -0.3645341 & 0.6945201 & 0.04581471 & n\\_race         & 1.3389950 & 0.1138776 & 0.9435616\\\\\n",
       "\tm & -0.3292454 & 0.7194664 & 0.05893603 & m              & 1.2296191 & 0.1138776 & 0.9435616\\\\\n",
       "\tm\\_race & -0.3034125 & 0.7382945 & 0.08674550 & m\\_race         & 1.0617531 & 0.1138776 & 0.9435616\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A5] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.3214742 & 0.7250794 & 0.12147231 & base           & 0.9155227 & 0.16075520 & 0.7938350\\\\\n",
       "\trace & -0.4119284 & 0.6623717 & 0.02639488 & race           & 1.5784803 & 0.07871222 & 1.1039578\\\\\n",
       "\tpriMalig & -0.4032794 & 0.6681254 & 0.02769500 & priMalig       & 1.5575987 & 0.07871222 & 1.1039578\\\\\n",
       "\tpriMalig\\_race & -0.4192504 & 0.6575396 & 0.02450147 & priMalig\\_race  & 1.6108078 & 0.07871222 & 1.1039578\\\\\n",
       "\ttreatment & -0.4018718 & 0.6690665 & 0.02811151 & treatment      & 1.5511159 & 0.07871222 & 1.1039578\\\\\n",
       "\ttreatment\\_race & -0.4166937 & 0.6592228 & 0.02515555 & treatment\\_race & 1.5993662 & 0.07871222 & 1.1039578\\\\\n",
       "\tstage & -0.2203511 & 0.8022371 & 0.28074113 & stage          & 0.5516939 & 0.28074113 & 0.5516939\\\\\n",
       "\tstage\\_race & -0.2294049 & 0.7950065 & 0.26790881 & stage\\_race     & 0.5720130 & 0.28074113 & 0.5516939\\\\\n",
       "\tt & -0.3050243 & 0.7371055 & 0.13728672 & t              & 0.8623715 & 0.16075520 & 0.7938350\\\\\n",
       "\tt\\_race & -0.3078603 & 0.7350180 & 0.13779017 & t\\_race         & 0.8607818 & 0.16075520 & 0.7938350\\\\\n",
       "\tn & -0.3794710 & 0.6842233 & 0.05398372 & n              & 1.2677372 & 0.08776018 & 1.0567025\\\\\n",
       "\tn\\_race & -0.3943869 & 0.6740932 & 0.04511138 & n\\_race         & 1.3457139 & 0.08776018 & 1.0567025\\\\\n",
       "\tm & -0.3447614 & 0.7083894 & 0.04984492 & m              & 1.3023791 & 0.08776018 & 1.0567025\\\\\n",
       "\tm\\_race & -0.3393600 & 0.7122260 & 0.05641726 & m\\_race         & 1.2485880 & 0.08776018 & 1.0567025\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A6] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.11754768 & 0.8890981 & 0.5191883 & base           & 0.28467513 & 0.8019877 & 0.09583231\\\\\n",
       "\trace & -0.04552751 & 0.9554933 & 0.7938364 & race           & 0.10026898 & 0.8019877 & 0.09583231\\\\\n",
       "\tpriMalig & -0.04464710 & 0.9563349 & 0.7949461 & priMalig       & 0.09966232 & 0.8019877 & 0.09583231\\\\\n",
       "\tpriMalig\\_race & -0.04448794 & 0.9564871 & 0.7976486 & priMalig\\_race  & 0.09818841 & 0.8019877 & 0.09583231\\\\\n",
       "\ttreatment & -0.04835582 & 0.9527947 & 0.7789184 & treatment      & 0.10850805 & 0.8019877 & 0.09583231\\\\\n",
       "\ttreatment\\_race & -0.04722964 & 0.9538683 & 0.7863848 & treatment\\_race & 0.10436486 & 0.8019877 & 0.09583231\\\\\n",
       "\tstage & -0.04675201 & 0.9543240 & 0.8019877 & stage          & 0.09583231 & 0.8019877 & 0.09583231\\\\\n",
       "\tstage\\_race & -0.05053270 & 0.9507228 & 0.7868357 & stage\\_race     & 0.10411595 & 0.8019877 & 0.09583231\\\\\n",
       "\tt & -0.21170254 & 0.8092054 & 0.2615762 & t              & 0.58240184 & 0.8019877 & 0.09583231\\\\\n",
       "\tt\\_race & -0.21568404 & 0.8059899 & 0.2536680 & t\\_race         & 0.59573423 & 0.8019877 & 0.09583231\\\\\n",
       "\tn & -0.15067649 & 0.8601259 & 0.3909433 & n              & 0.40788624 & 0.8019877 & 0.09583231\\\\\n",
       "\tn\\_race & -0.16811617 & 0.8452556 & 0.3442363 & n\\_race         & 0.46314330 & 0.8019877 & 0.09583231\\\\\n",
       "\tm & -0.15625882 & 0.8553378 & 0.3884121 & m              & 0.41070730 & 0.8019877 & 0.09583231\\\\\n",
       "\tm\\_race & -0.15169123 & 0.8592536 & 0.4066059 & m\\_race         & 0.39082635 & 0.8019877 & 0.09583231\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A8] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.08466804 & 0.9188172 & 0.6434806 & base           & 0.19146457 & 0.9240376 & 0.03431035\\\\\n",
       "\trace &  0.09806979 & 1.1030398 & 0.5506169 & race           & 0.25915045 & 0.9240376 & 0.03431035\\\\\n",
       "\tpriMalig &  0.10093490 & 1.1062046 & 0.5367857 & priMalig       & 0.27019905 & 0.9240376 & 0.03431035\\\\\n",
       "\tpriMalig\\_race &  0.09602066 & 1.1007818 & 0.5586265 & priMalig\\_race  & 0.25287845 & 0.9240376 & 0.03431035\\\\\n",
       "\ttreatment &  0.10100838 & 1.1062859 & 0.5357554 & treatment      & 0.27103348 & 0.9240376 & 0.03431035\\\\\n",
       "\ttreatment\\_race &  0.09713791 & 1.1020123 & 0.5538041 & treatment\\_race & 0.25664386 & 0.9240376 & 0.03431035\\\\\n",
       "\tstage & -0.01958032 & 0.9806101 & 0.9152165 & stage          & 0.03847617 & 0.9240376 & 0.03431035\\\\\n",
       "\tstage\\_race & -0.02761548 & 0.9727623 & 0.8801478 & stage\\_race     & 0.05544441 & 0.9240376 & 0.03431035\\\\\n",
       "\tt & -0.03636214 & 0.9642910 & 0.8462808 & t              & 0.07248551 & 0.9240376 & 0.03431035\\\\\n",
       "\tt\\_race & -0.04350308 & 0.9574296 & 0.8170881 & t\\_race         & 0.08773110 & 0.9240376 & 0.03431035\\\\\n",
       "\tn & -0.01612031 & 0.9840089 & 0.9240376 & n              & 0.03431035 & 0.9240376 & 0.03431035\\\\\n",
       "\tn\\_race & -0.03273859 & 0.9677915 & 0.8491155 & n\\_race         & 0.07103325 & 0.9240376 & 0.03431035\\\\\n",
       "\tm & -0.06656792 & 0.9355994 & 0.7062399 & m              & 0.15104774 & 0.9240376 & 0.03431035\\\\\n",
       "\tm\\_race & -0.08260712 & 0.9207128 & 0.6461885 & m\\_race         & 0.18964080 & 0.9240376 & 0.03431035\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A14] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.11599982 & 0.8904754 & 0.49459540 & base           & 0.3057499 & 0.5770280 & 0.2388031\\\\\n",
       "\trace & -0.23182824 & 0.7930823 & 0.16319892 & race           & 0.7872827 & 0.2827663 & 0.5485723\\\\\n",
       "\tpriMalig & -0.22081631 & 0.8018640 & 0.18198340 & priMalig       & 0.7399682 & 0.2827663 & 0.5485723\\\\\n",
       "\tpriMalig\\_race & -0.23473619 & 0.7907794 & 0.15651092 & priMalig\\_race  & 0.8054554 & 0.2827663 & 0.5485723\\\\\n",
       "\ttreatment & -0.21734938 & 0.8046488 & 0.19215218 & treatment      & 0.7163547 & 0.2827663 & 0.5485723\\\\\n",
       "\ttreatment\\_race & -0.23094084 & 0.7937864 & 0.16605555 & treatment\\_race & 0.7797466 & 0.2827663 & 0.5485723\\\\\n",
       "\tstage & -0.05644030 & 0.9451229 & 0.72883238 & stage          & 0.1373723 & 0.7288324 & 0.1373723\\\\\n",
       "\tstage\\_race & -0.06691126 & 0.9352782 & 0.68624395 & stage\\_race     & 0.1635215 & 0.7288324 & 0.1373723\\\\\n",
       "\tt & -0.19703065 & 0.8211655 & 0.21827773 & t              & 0.6609906 & 0.2827663 & 0.5485723\\\\\n",
       "\tt\\_race & -0.19785233 & 0.8204910 & 0.22217356 & t\\_race         & 0.6533076 & 0.2827663 & 0.5485723\\\\\n",
       "\tn & -0.30141010 & 0.7397743 & 0.07757237 & n              & 1.1102929 & 0.2827663 & 0.5485723\\\\\n",
       "\tn\\_race & -0.28448587 & 0.7524010 & 0.09836765 & n\\_race         & 1.0071477 & 0.2827663 & 0.5485723\\\\\n",
       "\tm & -0.29020266 & 0.7481119 & 0.08973468 & m              & 1.0470397 & 0.2827663 & 0.5485723\\\\\n",
       "\tm\\_race & -0.31136372 & 0.7324474 & 0.06869615 & m\\_race         & 1.1630676 & 0.2827663 & 0.5485723\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A13] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.3435566 & 0.7092433 & 0.0268222290 & base           & 1.571505 & 0.031292600 & 1.504558\\\\\n",
       "\trace & -0.4244354 & 0.6541390 & 0.0078498277 & race           & 2.105140 & 0.012654518 & 1.897754\\\\\n",
       "\tpriMalig & -0.4003243 & 0.6701027 & 0.0117622811 & priMalig       & 1.929508 & 0.015556029 & 1.808101\\\\\n",
       "\tpriMalig\\_race & -0.4317372 & 0.6493800 & 0.0069765180 & priMalig\\_race  & 2.156361 & 0.012654518 & 1.897754\\\\\n",
       "\ttreatment & -0.3997782 & 0.6704687 & 0.0122225943 & treatment      & 1.912837 & 0.015556029 & 1.808101\\\\\n",
       "\ttreatment\\_race & -0.4282103 & 0.6516744 & 0.0076029911 & treatment\\_race & 2.119016 & 0.012654518 & 1.897754\\\\\n",
       "\tstage & -0.2792254 & 0.7563694 & 0.0795272728 & stage          & 1.099484 & 0.079527273 & 1.099484\\\\\n",
       "\tstage\\_race & -0.3282254 & 0.7202007 & 0.0420689445 & stage\\_race     & 1.376038 & 0.045305017 & 1.343854\\\\\n",
       "\tt & -0.3998200 & 0.6704407 & 0.0081350474 & t              & 2.089640 & 0.012654518 & 1.897754\\\\\n",
       "\tt\\_race & -0.4324787 & 0.6488987 & 0.0047037037 & t\\_race         & 2.327560 & 0.012654518 & 1.897754\\\\\n",
       "\tn & -0.6066311 & 0.5451845 & 0.0002719233 & n              & 3.565554 & 0.001903463 & 2.720456\\\\\n",
       "\tn\\_race & -0.6220821 & 0.5368255 & 0.0001913499 & n\\_race         & 3.718172 & 0.001903463 & 2.720456\\\\\n",
       "\tm & -0.4396106 & 0.6442873 & 0.0063089152 & m              & 2.200045 & 0.012654518 & 1.897754\\\\\n",
       "\tm\\_race & -0.4638319 & 0.6288693 & 0.0043320615 & m\\_race         & 2.363305 & 0.012654518 & 1.897754\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A7] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.3106495 & 0.7329708 & 0.09701397 & base           & 1.0131657 & 0.1269405 & 0.8963998\\\\\n",
       "\trace & -0.3460993 & 0.7074423 & 0.04675172 & race           & 1.3302024 & 0.1096734 & 0.9598987\\\\\n",
       "\tpriMalig & -0.3158003 & 0.7292050 & 0.06267052 & priMalig       & 1.2029367 & 0.1096734 & 0.9598987\\\\\n",
       "\tpriMalig\\_race & -0.3451365 & 0.7081237 & 0.04743595 & priMalig\\_race  & 1.3238924 & 0.1096734 & 0.9598987\\\\\n",
       "\ttreatment & -0.3175991 & 0.7278946 & 0.06052995 & treatment      & 1.2180297 & 0.1096734 & 0.9598987\\\\\n",
       "\ttreatment\\_race & -0.3480718 & 0.7060482 & 0.04554466 & treatment\\_race & 1.3415625 & 0.1096734 & 0.9598987\\\\\n",
       "\tstage & -0.2092262 & 0.8112117 & 0.27034234 & stage          & 0.5680859 & 0.2703423 & 0.5680859\\\\\n",
       "\tstage\\_race & -0.2551871 & 0.7747715 & 0.19359950 & stage\\_race     & 0.7130958 & 0.2084918 & 0.6809111\\\\\n",
       "\tt & -0.2994011 & 0.7412621 & 0.11458166 & t              & 0.9408849 & 0.1336786 & 0.8739381\\\\\n",
       "\tt\\_race & -0.3196238 & 0.7264222 & 0.09973896 & t\\_race         & 1.0011352 & 0.1269405 & 0.8963998\\\\\n",
       "\tn & -0.4011960 & 0.6695188 & 0.02613977 & n              & 1.5826982 & 0.1096734 & 0.9598987\\\\\n",
       "\tn\\_race & -0.4223577 & 0.6554995 & 0.02070923 & n\\_race         & 1.6838360 & 0.1096734 & 0.9598987\\\\\n",
       "\tm & -0.3012729 & 0.7398759 & 0.08302095 & m              & 1.0808123 & 0.1269405 & 0.8963998\\\\\n",
       "\tm\\_race & -0.3292805 & 0.7194412 & 0.06255784 & m\\_race         & 1.2037182 & 0.1096734 & 0.9598987\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A17] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.1349697 & 0.8737424 & 0.35969783 & base           & 0.4440622 & 0.3596978 & 0.4440622\\\\\n",
       "\trace & -0.2726086 & 0.7613907 & 0.06952133 & race           & 1.1578819 & 0.1216623 & 0.9148438\\\\\n",
       "\tpriMalig & -0.2474031 & 0.7808259 & 0.09642004 & priMalig       & 1.0158327 & 0.1279569 & 0.8929364\\\\\n",
       "\tpriMalig\\_race & -0.2800633 & 0.7557359 & 0.06225612 & priMalig\\_race  & 1.2058179 & 0.1216623 & 0.9148438\\\\\n",
       "\ttreatment & -0.2464894 & 0.7815396 & 0.10053753 & treatment      & 0.9976718 & 0.1279569 & 0.8929364\\\\\n",
       "\ttreatment\\_race & -0.2757088 & 0.7590339 & 0.06802739 & treatment\\_race & 1.1673162 & 0.1216623 & 0.9148438\\\\\n",
       "\tstage & -0.2015116 & 0.8174941 & 0.16882792 & stage          & 0.7725557 & 0.1818147 & 0.7403711\\\\\n",
       "\tstage\\_race & -0.2582645 & 0.7723909 & 0.08204171 & stage\\_race     & 1.0859653 & 0.1276204 & 0.8940798\\\\\n",
       "\tt & -0.2253631 & 0.7982263 & 0.11036446 & t              & 0.9571708 & 0.1287585 & 0.8902240\\\\\n",
       "\tt\\_race & -0.2791573 & 0.7564209 & 0.05465295 & t\\_race         & 1.2623864 & 0.1216623 & 0.9148438\\\\\n",
       "\tn & -0.2929777 & 0.7460388 & 0.02961009 & n              & 1.5285603 & 0.1216623 & 0.9148438\\\\\n",
       "\tn\\_race & -0.3053085 & 0.7368960 & 0.03144463 & n\\_race         & 1.5024535 & 0.1216623 & 0.9148438\\\\\n",
       "\tm & -0.2778825 & 0.7573858 & 0.05459017 & m              & 1.2628856 & 0.1216623 & 0.9148438\\\\\n",
       "\tm\\_race & -0.3028784 & 0.7386889 & 0.04792513 & m\\_race         & 1.3194367 & 0.1216623 & 0.9148438\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A15] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & 0.2688539 & 1.308464 & 0.07434015 & base           & 1.1287765 & 0.21018728 & 0.6773936\\\\\n",
       "\trace & 0.2128963 & 1.237256 & 0.17151090 & race           & 0.7657083 & 0.21018728 & 0.6773936\\\\\n",
       "\tpriMalig & 0.2119131 & 1.236040 & 0.16761025 & priMalig       & 0.7756994 & 0.21018728 & 0.6773936\\\\\n",
       "\tpriMalig\\_race & 0.2110181 & 1.234935 & 0.17494071 & priMalig\\_race  & 0.7571091 & 0.21018728 & 0.6773936\\\\\n",
       "\ttreatment & 0.2242117 & 1.251336 & 0.14627079 & treatment      & 0.8348424 & 0.21018728 & 0.6773936\\\\\n",
       "\ttreatment\\_race & 0.2231841 & 1.250051 & 0.15574360 & treatment\\_race & 0.8075898 & 0.21018728 & 0.6773936\\\\\n",
       "\tstage & 0.4197153 & 1.521528 & 0.00881893 & stage          & 2.0545841 & 0.06173251 & 1.2094861\\\\\n",
       "\tstage\\_race & 0.4298565 & 1.537037 & 0.00791663 & stage\\_race     & 2.1014596 & 0.06173251 & 1.2094861\\\\\n",
       "\tt & 0.1861073 & 1.204551 & 0.21018728 & t              & 0.6773936 & 0.21018728 & 0.6773936\\\\\n",
       "\tt\\_race & 0.1975572 & 1.218423 & 0.18670321 & t\\_race         & 0.7288482 & 0.21018728 & 0.6773936\\\\\n",
       "\tn & 0.2039938 & 1.226291 & 0.20274418 & n              & 0.6930516 & 0.21018728 & 0.6773936\\\\\n",
       "\tn\\_race & 0.2381646 & 1.268918 & 0.14517008 & n\\_race         & 0.8381229 & 0.21018728 & 0.6773936\\\\\n",
       "\tm & 0.2343360 & 1.264069 & 0.14160843 & m              & 0.8489109 & 0.21018728 & 0.6773936\\\\\n",
       "\tm\\_race & 0.2446312 & 1.277150 & 0.13075190 & m\\_race         & 0.8835520 & 0.21018728 & 0.6773936\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A11] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.3635193 & 0.6952253 & 0.03206192 & base           & 1.494010 & 0.05481908 & 1.261068\\\\\n",
       "\trace & -0.3119401 & 0.7320254 & 0.04698779 & race           & 1.328015 & 0.05481908 & 1.261068\\\\\n",
       "\tpriMalig & -0.3122723 & 0.7317823 & 0.04485897 & priMalig       & 1.348151 & 0.05481908 & 1.261068\\\\\n",
       "\tpriMalig\\_race & -0.3180000 & 0.7276028 & 0.04321942 & priMalig\\_race  & 1.364321 & 0.05481908 & 1.261068\\\\\n",
       "\ttreatment & -0.3084579 & 0.7345788 & 0.04691170 & treatment      & 1.328719 & 0.05481908 & 1.261068\\\\\n",
       "\ttreatment\\_race & -0.3136502 & 0.7307746 & 0.04603630 & treatment\\_race & 1.336900 & 0.05481908 & 1.261068\\\\\n",
       "\tstage & -0.3936648 & 0.6745801 & 0.02177259 & stage          & 1.662090 & 0.05481908 & 1.261068\\\\\n",
       "\tstage\\_race & -0.4057206 & 0.6664963 & 0.01657187 & stage\\_race     & 1.780629 & 0.05481908 & 1.261068\\\\\n",
       "\tt & -0.3516416 & 0.7035322 & 0.04378668 & t              & 1.358658 & 0.05481908 & 1.261068\\\\\n",
       "\tt\\_race & -0.3623216 & 0.6960585 & 0.03486353 & t\\_race         & 1.457629 & 0.05481908 & 1.261068\\\\\n",
       "\tn & -0.3941567 & 0.6742484 & 0.01178682 & n              & 1.928603 & 0.05481908 & 1.261068\\\\\n",
       "\tn\\_race & -0.4026736 & 0.6685303 & 0.01106625 & n\\_race         & 1.956000 & 0.05481908 & 1.261068\\\\\n",
       "\tm & -0.2850493 & 0.7519771 & 0.06356282 & m              & 1.196797 & 0.06845226 & 1.164612\\\\\n",
       "\tm\\_race & -0.2825700 & 0.7538438 & 0.06850734 & m\\_race         & 1.164263 & 0.06850734 & 1.164263\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A23] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & 0.4306382 & 1.538239 & 0.014916256 & base           & 1.826340 & 0.02610345 & 1.583302\\\\\n",
       "\trace & 0.4202414 & 1.522329 & 0.010141267 & race           & 1.993908 & 0.02059789 & 1.686177\\\\\n",
       "\tpriMalig & 0.4154642 & 1.515074 & 0.010298946 & priMalig       & 1.987207 & 0.02059789 & 1.686177\\\\\n",
       "\tpriMalig\\_race & 0.4219007 & 1.524857 & 0.009939883 & priMalig\\_race  & 2.002619 & 0.02059789 & 1.686177\\\\\n",
       "\ttreatment & 0.4147639 & 1.514013 & 0.010234579 & treatment      & 1.989930 & 0.02059789 & 1.686177\\\\\n",
       "\ttreatment\\_race & 0.4222213 & 1.525346 & 0.009751555 & treatment\\_race & 2.010926 & 0.02059789 & 1.686177\\\\\n",
       "\tstage & 0.4879161 & 1.628918 & 0.003616781 & stage          & 2.441678 & 0.02059789 & 1.686177\\\\\n",
       "\tstage\\_race & 0.5033201 & 1.654204 & 0.002904347 & stage\\_race     & 2.536951 & 0.02059789 & 1.686177\\\\\n",
       "\tt & 0.3296883 & 1.390535 & 0.062993722 & t              & 1.200703 & 0.06849331 & 1.164352\\\\\n",
       "\tt\\_race & 0.3251847 & 1.384286 & 0.068938410 & t\\_race         & 1.161539 & 0.06893841 & 1.161539\\\\\n",
       "\tn & 0.3456039 & 1.412843 & 0.057023115 & n              & 1.243949 & 0.06849331 & 1.164352\\\\\n",
       "\tn\\_race & 0.3424405 & 1.408381 & 0.063600927 & n\\_race         & 1.196537 & 0.06849331 & 1.164352\\\\\n",
       "\tm & 0.3571492 & 1.429249 & 0.042185009 & m              & 1.374842 & 0.05905901 & 1.228714\\\\\n",
       "\tm\\_race & 0.3671640 & 1.443635 & 0.037741551 & m\\_race         & 1.423180 & 0.05870908 & 1.231295\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A12] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.4526312 & 0.6359526 & 0.010797048 & base           & 1.966695 & 0.02797277 & 1.553265\\\\\n",
       "\trace & -0.3617442 & 0.6964605 & 0.024944324 & race           & 1.603028 & 0.02797277 & 1.553265\\\\\n",
       "\tpriMalig & -0.3486193 & 0.7056617 & 0.025974714 & priMalig       & 1.585449 & 0.02797277 & 1.553265\\\\\n",
       "\tpriMalig\\_race & -0.3677997 & 0.6922558 & 0.022361922 & priMalig\\_race  & 1.650491 & 0.02797277 & 1.553265\\\\\n",
       "\ttreatment & -0.3441197 & 0.7088441 & 0.028489127 & treatment      & 1.545321 & 0.02848913 & 1.545321\\\\\n",
       "\ttreatment\\_race & -0.3612062 & 0.6968353 & 0.025152561 & treatment\\_race & 1.599418 & 0.02797277 & 1.553265\\\\\n",
       "\tstage & -0.3945856 & 0.6739593 & 0.024839065 & stage          & 1.604865 & 0.02797277 & 1.553265\\\\\n",
       "\tstage\\_race & -0.4353729 & 0.6470234 & 0.015185631 & stage\\_race     & 1.818567 & 0.02797277 & 1.553265\\\\\n",
       "\tt & -0.4476685 & 0.6391165 & 0.014061958 & t              & 1.851954 & 0.02797277 & 1.553265\\\\\n",
       "\tt\\_race & -0.4686108 & 0.6258712 & 0.011283793 & t\\_race         & 1.947545 & 0.02797277 & 1.553265\\\\\n",
       "\tn & -0.4513201 & 0.6367870 & 0.004655780 & n              & 2.332008 & 0.02797277 & 1.553265\\\\\n",
       "\tn\\_race & -0.4526342 & 0.6359507 & 0.005398379 & n\\_race         & 2.267737 & 0.02797277 & 1.553265\\\\\n",
       "\tm & -0.3847837 & 0.6805978 & 0.016652670 & m              & 1.778516 & 0.02797277 & 1.553265\\\\\n",
       "\tm\\_race & -0.3973236 & 0.6721165 & 0.015958008 & m\\_race         & 1.797021 & 0.02797277 & 1.553265\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SLC22A24] A data.frame: 14 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & coef & hazard & pval & comp & negLog.pval & padj & negLog.padj\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbase & -0.6141615 & 0.5410944 & 7.279835e-04 & base           & 3.137878 & 0.0010352591 & 2.984951\\\\\n",
       "\trace & -0.6056222 & 0.5457347 & 5.743739e-04 & race           & 3.240805 & 0.0010352591 & 2.984951\\\\\n",
       "\tpriMalig & -0.5916532 & 0.5534116 & 6.834294e-04 & priMalig       & 3.165306 & 0.0010352591 & 2.984951\\\\\n",
       "\tpriMalig\\_race & -0.6179197 & 0.5390647 & 5.080191e-04 & priMalig\\_race  & 3.294120 & 0.0010352591 & 2.984951\\\\\n",
       "\ttreatment & -0.5801044 & 0.5598399 & 7.394708e-04 & treatment      & 3.131079 & 0.0010352591 & 2.984951\\\\\n",
       "\ttreatment\\_race & -0.6077782 & 0.5445594 & 5.399233e-04 & treatment\\_race & 3.267668 & 0.0010352591 & 2.984951\\\\\n",
       "\tstage & -0.7178532 & 0.4877984 & 4.989184e-05 & stage          & 4.301970 & 0.0003492429 & 3.456872\\\\\n",
       "\tstage\\_race & -0.7573798 & 0.4688934 & 3.263103e-05 & stage\\_race     & 4.486369 & 0.0003492429 & 3.456872\\\\\n",
       "\tt & -0.5256927 & 0.5911457 & 3.794892e-03 & t              & 2.420801 & 0.0037948915 & 2.420801\\\\\n",
       "\tt\\_race & -0.5413052 & 0.5819881 & 3.251946e-03 & t\\_race         & 2.487857 & 0.0035020960 & 2.455672\\\\\n",
       "\tn & -0.6398630 & 0.5273647 & 3.230589e-04 & n              & 3.490718 & 0.0010352591 & 2.984951\\\\\n",
       "\tn\\_race & -0.7126921 & 0.4903224 & 9.898186e-05 & n\\_race         & 4.004444 & 0.0004619153 & 3.335438\\\\\n",
       "\tm & -0.5309554 & 0.5880429 & 1.925392e-03 & m              & 2.715481 & 0.0022462908 & 2.648534\\\\\n",
       "\tm\\_race & -0.5649519 & 0.5683875 & 1.263062e-03 & m\\_race         & 2.898575 & 0.0016075339 & 2.793840\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$SLC22A18\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.5529791 | 0.5752336 | 4.015022e-04 | base           | 3.396312 | 0.0014052576 | 2.852244 |\n",
       "| race | -0.4555792 | 0.6340806 | 5.443624e-03 | race           | 2.264112 | 0.0071820852 | 2.143749 |\n",
       "| priMalig | -0.4597203 | 0.6314602 | 5.643067e-03 | priMalig       | 2.248485 | 0.0071820852 | 2.143749 |\n",
       "| priMalig_race | -0.4671379 | 0.6267937 | 4.542739e-03 | priMalig_race  | 2.342682 | 0.0071820852 | 2.143749 |\n",
       "| treatment | -0.4485935 | 0.6385256 | 6.722625e-03 | treatment      | 2.172461 | 0.0078430627 | 2.105514 |\n",
       "| treatment_race | -0.4551692 | 0.6343406 | 5.563081e-03 | treatment_race | 2.254685 | 0.0071820852 | 2.143749 |\n",
       "| stage | -0.5224717 | 0.5930529 | 5.201296e-04 | stage          | 3.283888 | 0.0014563628 | 2.836730 |\n",
       "| stage_race | -0.5370928 | 0.5844449 | 3.296065e-04 | stage_race     | 3.482004 | 0.0014052576 | 2.852244 |\n",
       "| t | -0.6267465 | 0.5343274 | 1.386588e-04 | t              | 3.858053 | 0.0009706116 | 3.012955 |\n",
       "| t_race | -0.6503880 | 0.5218433 | 7.700767e-05 | t_race         | 4.113466 | 0.0009706116 | 3.012955 |\n",
       "| n | -0.4233537 | 0.6548469 | 4.340832e-03 | n              | 2.362427 | 0.0071820852 | 2.143749 |\n",
       "| n_race | -0.4245808 | 0.6540439 | 4.014569e-03 | n_race         | 2.396361 | 0.0071820852 | 2.143749 |\n",
       "| m | -0.4336793 | 0.6481201 | 9.557998e-03 | m              | 2.019633 | 0.0095579976 | 2.019633 |\n",
       "| m_race | -0.4434774 | 0.6418007 | 8.342631e-03 | m_race         | 2.078697 | 0.0089843718 | 2.046512 |\n",
       "\n",
       "\n",
       "$SLC22A1\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | 0.13474132 | 1.144241 | 0.3288761 | base           | 0.48296774 | 0.7820991 | 0.10673820 |\n",
       "| race | 0.10537154 | 1.111123 | 0.4804395 | race           | 0.31836134 | 0.7820991 | 0.10673820 |\n",
       "| priMalig | 0.10310895 | 1.108612 | 0.4824802 | priMalig       | 0.31652055 | 0.7820991 | 0.10673820 |\n",
       "| priMalig_race | 0.10249956 | 1.107937 | 0.4926811 | priMalig_race  | 0.30743406 | 0.7820991 | 0.10673820 |\n",
       "| treatment | 0.11160181 | 1.118068 | 0.4493267 | treatment      | 0.34743780 | 0.7820991 | 0.10673820 |\n",
       "| treatment_race | 0.11150145 | 1.117955 | 0.4579416 | treatment_race | 0.33918989 | 0.7820991 | 0.10673820 |\n",
       "| stage | 0.25554519 | 1.291165 | 0.1033279 | stage          | 0.98578258 | 0.7444004 | 0.12819340 |\n",
       "| stage_race | 0.25689877 | 1.292914 | 0.1063429 | stage_race     | 0.97329144 | 0.7444004 | 0.12819340 |\n",
       "| t | 0.00388421 | 1.003892 | 0.9769607 | t              | 0.01012292 | 0.9769607 | 0.01012292 |\n",
       "| t_race | 0.01717292 | 1.017321 | 0.9004880 | t_race         | 0.04552209 | 0.9697563 | 0.01333740 |\n",
       "| n | 0.08478981 | 1.088488 | 0.5507965 | n              | 0.25900882 | 0.7820991 | 0.10673820 |\n",
       "| n_race | 0.07472680 | 1.077590 | 0.6070861 | n_race         | 0.21674969 | 0.7820991 | 0.10673820 |\n",
       "| m | 0.07591994 | 1.078876 | 0.6171934 | m              | 0.20957870 | 0.7820991 | 0.10673820 |\n",
       "| m_race | 0.06555997 | 1.067757 | 0.6703707 | m_race         | 0.17368499 | 0.7820991 | 0.10673820 |\n",
       "\n",
       "\n",
       "$SLC22A3\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.1576155 | 0.8541781 | 0.38488802 | base           | 0.4146656 | 0.44903602 | 0.3477188 |\n",
       "| race | -0.3487025 | 0.7056030 | 0.02978642 | race           | 1.5259816 | 0.08782176 | 1.0563979 |\n",
       "| priMalig | -0.3618447 | 0.6963905 | 0.02634301 | priMalig       | 1.5793345 | 0.08782176 | 1.0563979 |\n",
       "| priMalig_race | -0.3480427 | 0.7060687 | 0.03136491 | priMalig_race  | 1.5035559 | 0.08782176 | 1.0563979 |\n",
       "| treatment | -0.3720835 | 0.6892967 | 0.02353388 | treatment      | 1.6283064 | 0.08782176 | 1.0563979 |\n",
       "| treatment_race | -0.3585744 | 0.6986716 | 0.02775584 | treatment_race | 1.5566456 | 0.08782176 | 1.0563979 |\n",
       "| stage | -0.1356536 | 0.8731450 | 0.46334024 | stage          | 0.3341000 | 0.46334024 | 0.3341000 |\n",
       "| stage_race | -0.1379468 | 0.8711451 | 0.45206244 | stage_race     | 0.3448016 | 0.46334024 | 0.3341000 |\n",
       "| t | -0.2888458 | 0.7491277 | 0.09247548 | t              | 1.0339734 | 0.16183209 | 0.7909354 |\n",
       "| t_race | -0.2758034 | 0.7589621 | 0.11050628 | t_race         | 0.9566130 | 0.17189866 | 0.7647275 |\n",
       "| n | -0.1865966 | 0.8297784 | 0.28685219 | n              | 0.5423418 | 0.40159307 | 0.3962138 |\n",
       "| n_race | -0.1714353 | 0.8424548 | 0.32561348 | n_race         | 0.4872976 | 0.41441715 | 0.3825623 |\n",
       "| m | -0.3065829 | 0.7359575 | 0.07736576 | m              | 1.1114512 | 0.16183209 | 0.7909354 |\n",
       "| m_race | -0.2903929 | 0.7479696 | 0.08996423 | m_race         | 1.0459301 | 0.16183209 | 0.7909354 |\n",
       "\n",
       "\n",
       "$SLC22A2\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.7817740 | 0.4575935 | 3.550625e-05 | base           | 4.449695 | 3.550625e-05 | 4.449695 |\n",
       "| race | -0.8852318 | 0.4126185 | 2.829581e-06 | race           | 5.548278 | 5.753629e-06 | 5.240058 |\n",
       "| priMalig | -0.9105052 | 0.4023209 | 1.096492e-06 | priMalig       | 5.959994 | 5.753629e-06 | 5.240058 |\n",
       "| priMalig_race | -0.8863224 | 0.4121688 | 2.294701e-06 | priMalig_race  | 5.639274 | 5.753629e-06 | 5.240058 |\n",
       "| treatment | -0.9087330 | 0.4030346 | 1.376105e-06 | treatment      | 5.861348 | 5.753629e-06 | 5.240058 |\n",
       "| treatment_race | -0.8841183 | 0.4130782 | 2.876815e-06 | treatment_race | 5.541088 | 5.753629e-06 | 5.240058 |\n",
       "| stage | -0.8608859 | 0.4227874 | 1.695237e-06 | stage          | 5.770770 | 5.753629e-06 | 5.240058 |\n",
       "| stage_race | -0.8513621 | 0.4268331 | 1.434635e-06 | stage_race     | 5.843259 | 5.753629e-06 | 5.240058 |\n",
       "| t | -0.8081409 | 0.4456859 | 1.141854e-05 | t              | 4.942389 | 1.818226e-05 | 4.740352 |\n",
       "| t_race | -0.7879744 | 0.4547650 | 1.409344e-05 | t_race         | 4.850983 | 1.973082e-05 | 4.704855 |\n",
       "| n | -0.7515369 | 0.4716412 | 3.471360e-05 | n              | 4.459500 | 3.550625e-05 | 4.449695 |\n",
       "| n_race | -0.7455377 | 0.4744791 | 3.207595e-05 | n_race         | 4.493820 | 3.550625e-05 | 4.449695 |\n",
       "| m | -0.8844331 | 0.4129482 | 1.168860e-05 | m              | 4.932238 | 1.818226e-05 | 4.740352 |\n",
       "| m_race | -0.8445918 | 0.4297328 | 2.523022e-05 | m_race         | 4.598079 | 3.211119e-05 | 4.493344 |\n",
       "\n",
       "\n",
       "$SLC22A4\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.2961243 | 0.7436950 | 0.08900649 | base           | 1.0505783 | 0.1138776 | 0.9435616 |\n",
       "| race | -0.2962588 | 0.7435950 | 0.10163499 | race           | 0.9929568 | 0.1138776 | 0.9435616 |\n",
       "| priMalig | -0.3040131 | 0.7378512 | 0.08917201 | priMalig       | 1.0497715 | 0.1138776 | 0.9435616 |\n",
       "| priMalig_race | -0.2933213 | 0.7457825 | 0.10739933 | priMalig_race  | 0.9689984 | 0.1138776 | 0.9435616 |\n",
       "| treatment | -0.3168036 | 0.7284738 | 0.07605500 | treatment      | 1.1188722 | 0.1138776 | 0.9435616 |\n",
       "| treatment_race | -0.3057072 | 0.7366022 | 0.09262746 | treatment_race | 1.0332602 | 0.1138776 | 0.9435616 |\n",
       "| stage | -0.2788685 | 0.7566394 | 0.09631543 | stage          | 1.0163041 | 0.1138776 | 0.9435616 |\n",
       "| stage_race | -0.2777735 | 0.7574684 | 0.10214153 | stage_race     | 0.9907977 | 0.1138776 | 0.9435616 |\n",
       "| t | -0.2870936 | 0.7504414 | 0.09764978 | t              | 1.0103287 | 0.1138776 | 0.9435616 |\n",
       "| t_race | -0.2771767 | 0.7579206 | 0.11387763 | t_race         | 0.9435616 | 0.1138776 | 0.9435616 |\n",
       "| n | -0.3557770 | 0.7006288 | 0.04814394 | n              | 1.3174584 | 0.1138776 | 0.9435616 |\n",
       "| n_race | -0.3645341 | 0.6945201 | 0.04581471 | n_race         | 1.3389950 | 0.1138776 | 0.9435616 |\n",
       "| m | -0.3292454 | 0.7194664 | 0.05893603 | m              | 1.2296191 | 0.1138776 | 0.9435616 |\n",
       "| m_race | -0.3034125 | 0.7382945 | 0.08674550 | m_race         | 1.0617531 | 0.1138776 | 0.9435616 |\n",
       "\n",
       "\n",
       "$SLC22A5\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.3214742 | 0.7250794 | 0.12147231 | base           | 0.9155227 | 0.16075520 | 0.7938350 |\n",
       "| race | -0.4119284 | 0.6623717 | 0.02639488 | race           | 1.5784803 | 0.07871222 | 1.1039578 |\n",
       "| priMalig | -0.4032794 | 0.6681254 | 0.02769500 | priMalig       | 1.5575987 | 0.07871222 | 1.1039578 |\n",
       "| priMalig_race | -0.4192504 | 0.6575396 | 0.02450147 | priMalig_race  | 1.6108078 | 0.07871222 | 1.1039578 |\n",
       "| treatment | -0.4018718 | 0.6690665 | 0.02811151 | treatment      | 1.5511159 | 0.07871222 | 1.1039578 |\n",
       "| treatment_race | -0.4166937 | 0.6592228 | 0.02515555 | treatment_race | 1.5993662 | 0.07871222 | 1.1039578 |\n",
       "| stage | -0.2203511 | 0.8022371 | 0.28074113 | stage          | 0.5516939 | 0.28074113 | 0.5516939 |\n",
       "| stage_race | -0.2294049 | 0.7950065 | 0.26790881 | stage_race     | 0.5720130 | 0.28074113 | 0.5516939 |\n",
       "| t | -0.3050243 | 0.7371055 | 0.13728672 | t              | 0.8623715 | 0.16075520 | 0.7938350 |\n",
       "| t_race | -0.3078603 | 0.7350180 | 0.13779017 | t_race         | 0.8607818 | 0.16075520 | 0.7938350 |\n",
       "| n | -0.3794710 | 0.6842233 | 0.05398372 | n              | 1.2677372 | 0.08776018 | 1.0567025 |\n",
       "| n_race | -0.3943869 | 0.6740932 | 0.04511138 | n_race         | 1.3457139 | 0.08776018 | 1.0567025 |\n",
       "| m | -0.3447614 | 0.7083894 | 0.04984492 | m              | 1.3023791 | 0.08776018 | 1.0567025 |\n",
       "| m_race | -0.3393600 | 0.7122260 | 0.05641726 | m_race         | 1.2485880 | 0.08776018 | 1.0567025 |\n",
       "\n",
       "\n",
       "$SLC22A6\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.11754768 | 0.8890981 | 0.5191883 | base           | 0.28467513 | 0.8019877 | 0.09583231 |\n",
       "| race | -0.04552751 | 0.9554933 | 0.7938364 | race           | 0.10026898 | 0.8019877 | 0.09583231 |\n",
       "| priMalig | -0.04464710 | 0.9563349 | 0.7949461 | priMalig       | 0.09966232 | 0.8019877 | 0.09583231 |\n",
       "| priMalig_race | -0.04448794 | 0.9564871 | 0.7976486 | priMalig_race  | 0.09818841 | 0.8019877 | 0.09583231 |\n",
       "| treatment | -0.04835582 | 0.9527947 | 0.7789184 | treatment      | 0.10850805 | 0.8019877 | 0.09583231 |\n",
       "| treatment_race | -0.04722964 | 0.9538683 | 0.7863848 | treatment_race | 0.10436486 | 0.8019877 | 0.09583231 |\n",
       "| stage | -0.04675201 | 0.9543240 | 0.8019877 | stage          | 0.09583231 | 0.8019877 | 0.09583231 |\n",
       "| stage_race | -0.05053270 | 0.9507228 | 0.7868357 | stage_race     | 0.10411595 | 0.8019877 | 0.09583231 |\n",
       "| t | -0.21170254 | 0.8092054 | 0.2615762 | t              | 0.58240184 | 0.8019877 | 0.09583231 |\n",
       "| t_race | -0.21568404 | 0.8059899 | 0.2536680 | t_race         | 0.59573423 | 0.8019877 | 0.09583231 |\n",
       "| n | -0.15067649 | 0.8601259 | 0.3909433 | n              | 0.40788624 | 0.8019877 | 0.09583231 |\n",
       "| n_race | -0.16811617 | 0.8452556 | 0.3442363 | n_race         | 0.46314330 | 0.8019877 | 0.09583231 |\n",
       "| m | -0.15625882 | 0.8553378 | 0.3884121 | m              | 0.41070730 | 0.8019877 | 0.09583231 |\n",
       "| m_race | -0.15169123 | 0.8592536 | 0.4066059 | m_race         | 0.39082635 | 0.8019877 | 0.09583231 |\n",
       "\n",
       "\n",
       "$SLC22A8\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.08466804 | 0.9188172 | 0.6434806 | base           | 0.19146457 | 0.9240376 | 0.03431035 |\n",
       "| race |  0.09806979 | 1.1030398 | 0.5506169 | race           | 0.25915045 | 0.9240376 | 0.03431035 |\n",
       "| priMalig |  0.10093490 | 1.1062046 | 0.5367857 | priMalig       | 0.27019905 | 0.9240376 | 0.03431035 |\n",
       "| priMalig_race |  0.09602066 | 1.1007818 | 0.5586265 | priMalig_race  | 0.25287845 | 0.9240376 | 0.03431035 |\n",
       "| treatment |  0.10100838 | 1.1062859 | 0.5357554 | treatment      | 0.27103348 | 0.9240376 | 0.03431035 |\n",
       "| treatment_race |  0.09713791 | 1.1020123 | 0.5538041 | treatment_race | 0.25664386 | 0.9240376 | 0.03431035 |\n",
       "| stage | -0.01958032 | 0.9806101 | 0.9152165 | stage          | 0.03847617 | 0.9240376 | 0.03431035 |\n",
       "| stage_race | -0.02761548 | 0.9727623 | 0.8801478 | stage_race     | 0.05544441 | 0.9240376 | 0.03431035 |\n",
       "| t | -0.03636214 | 0.9642910 | 0.8462808 | t              | 0.07248551 | 0.9240376 | 0.03431035 |\n",
       "| t_race | -0.04350308 | 0.9574296 | 0.8170881 | t_race         | 0.08773110 | 0.9240376 | 0.03431035 |\n",
       "| n | -0.01612031 | 0.9840089 | 0.9240376 | n              | 0.03431035 | 0.9240376 | 0.03431035 |\n",
       "| n_race | -0.03273859 | 0.9677915 | 0.8491155 | n_race         | 0.07103325 | 0.9240376 | 0.03431035 |\n",
       "| m | -0.06656792 | 0.9355994 | 0.7062399 | m              | 0.15104774 | 0.9240376 | 0.03431035 |\n",
       "| m_race | -0.08260712 | 0.9207128 | 0.6461885 | m_race         | 0.18964080 | 0.9240376 | 0.03431035 |\n",
       "\n",
       "\n",
       "$SLC22A14\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.11599982 | 0.8904754 | 0.49459540 | base           | 0.3057499 | 0.5770280 | 0.2388031 |\n",
       "| race | -0.23182824 | 0.7930823 | 0.16319892 | race           | 0.7872827 | 0.2827663 | 0.5485723 |\n",
       "| priMalig | -0.22081631 | 0.8018640 | 0.18198340 | priMalig       | 0.7399682 | 0.2827663 | 0.5485723 |\n",
       "| priMalig_race | -0.23473619 | 0.7907794 | 0.15651092 | priMalig_race  | 0.8054554 | 0.2827663 | 0.5485723 |\n",
       "| treatment | -0.21734938 | 0.8046488 | 0.19215218 | treatment      | 0.7163547 | 0.2827663 | 0.5485723 |\n",
       "| treatment_race | -0.23094084 | 0.7937864 | 0.16605555 | treatment_race | 0.7797466 | 0.2827663 | 0.5485723 |\n",
       "| stage | -0.05644030 | 0.9451229 | 0.72883238 | stage          | 0.1373723 | 0.7288324 | 0.1373723 |\n",
       "| stage_race | -0.06691126 | 0.9352782 | 0.68624395 | stage_race     | 0.1635215 | 0.7288324 | 0.1373723 |\n",
       "| t | -0.19703065 | 0.8211655 | 0.21827773 | t              | 0.6609906 | 0.2827663 | 0.5485723 |\n",
       "| t_race | -0.19785233 | 0.8204910 | 0.22217356 | t_race         | 0.6533076 | 0.2827663 | 0.5485723 |\n",
       "| n | -0.30141010 | 0.7397743 | 0.07757237 | n              | 1.1102929 | 0.2827663 | 0.5485723 |\n",
       "| n_race | -0.28448587 | 0.7524010 | 0.09836765 | n_race         | 1.0071477 | 0.2827663 | 0.5485723 |\n",
       "| m | -0.29020266 | 0.7481119 | 0.08973468 | m              | 1.0470397 | 0.2827663 | 0.5485723 |\n",
       "| m_race | -0.31136372 | 0.7324474 | 0.06869615 | m_race         | 1.1630676 | 0.2827663 | 0.5485723 |\n",
       "\n",
       "\n",
       "$SLC22A13\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.3435566 | 0.7092433 | 0.0268222290 | base           | 1.571505 | 0.031292600 | 1.504558 |\n",
       "| race | -0.4244354 | 0.6541390 | 0.0078498277 | race           | 2.105140 | 0.012654518 | 1.897754 |\n",
       "| priMalig | -0.4003243 | 0.6701027 | 0.0117622811 | priMalig       | 1.929508 | 0.015556029 | 1.808101 |\n",
       "| priMalig_race | -0.4317372 | 0.6493800 | 0.0069765180 | priMalig_race  | 2.156361 | 0.012654518 | 1.897754 |\n",
       "| treatment | -0.3997782 | 0.6704687 | 0.0122225943 | treatment      | 1.912837 | 0.015556029 | 1.808101 |\n",
       "| treatment_race | -0.4282103 | 0.6516744 | 0.0076029911 | treatment_race | 2.119016 | 0.012654518 | 1.897754 |\n",
       "| stage | -0.2792254 | 0.7563694 | 0.0795272728 | stage          | 1.099484 | 0.079527273 | 1.099484 |\n",
       "| stage_race | -0.3282254 | 0.7202007 | 0.0420689445 | stage_race     | 1.376038 | 0.045305017 | 1.343854 |\n",
       "| t | -0.3998200 | 0.6704407 | 0.0081350474 | t              | 2.089640 | 0.012654518 | 1.897754 |\n",
       "| t_race | -0.4324787 | 0.6488987 | 0.0047037037 | t_race         | 2.327560 | 0.012654518 | 1.897754 |\n",
       "| n | -0.6066311 | 0.5451845 | 0.0002719233 | n              | 3.565554 | 0.001903463 | 2.720456 |\n",
       "| n_race | -0.6220821 | 0.5368255 | 0.0001913499 | n_race         | 3.718172 | 0.001903463 | 2.720456 |\n",
       "| m | -0.4396106 | 0.6442873 | 0.0063089152 | m              | 2.200045 | 0.012654518 | 1.897754 |\n",
       "| m_race | -0.4638319 | 0.6288693 | 0.0043320615 | m_race         | 2.363305 | 0.012654518 | 1.897754 |\n",
       "\n",
       "\n",
       "$SLC22A7\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.3106495 | 0.7329708 | 0.09701397 | base           | 1.0131657 | 0.1269405 | 0.8963998 |\n",
       "| race | -0.3460993 | 0.7074423 | 0.04675172 | race           | 1.3302024 | 0.1096734 | 0.9598987 |\n",
       "| priMalig | -0.3158003 | 0.7292050 | 0.06267052 | priMalig       | 1.2029367 | 0.1096734 | 0.9598987 |\n",
       "| priMalig_race | -0.3451365 | 0.7081237 | 0.04743595 | priMalig_race  | 1.3238924 | 0.1096734 | 0.9598987 |\n",
       "| treatment | -0.3175991 | 0.7278946 | 0.06052995 | treatment      | 1.2180297 | 0.1096734 | 0.9598987 |\n",
       "| treatment_race | -0.3480718 | 0.7060482 | 0.04554466 | treatment_race | 1.3415625 | 0.1096734 | 0.9598987 |\n",
       "| stage | -0.2092262 | 0.8112117 | 0.27034234 | stage          | 0.5680859 | 0.2703423 | 0.5680859 |\n",
       "| stage_race | -0.2551871 | 0.7747715 | 0.19359950 | stage_race     | 0.7130958 | 0.2084918 | 0.6809111 |\n",
       "| t | -0.2994011 | 0.7412621 | 0.11458166 | t              | 0.9408849 | 0.1336786 | 0.8739381 |\n",
       "| t_race | -0.3196238 | 0.7264222 | 0.09973896 | t_race         | 1.0011352 | 0.1269405 | 0.8963998 |\n",
       "| n | -0.4011960 | 0.6695188 | 0.02613977 | n              | 1.5826982 | 0.1096734 | 0.9598987 |\n",
       "| n_race | -0.4223577 | 0.6554995 | 0.02070923 | n_race         | 1.6838360 | 0.1096734 | 0.9598987 |\n",
       "| m | -0.3012729 | 0.7398759 | 0.08302095 | m              | 1.0808123 | 0.1269405 | 0.8963998 |\n",
       "| m_race | -0.3292805 | 0.7194412 | 0.06255784 | m_race         | 1.2037182 | 0.1096734 | 0.9598987 |\n",
       "\n",
       "\n",
       "$SLC22A17\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.1349697 | 0.8737424 | 0.35969783 | base           | 0.4440622 | 0.3596978 | 0.4440622 |\n",
       "| race | -0.2726086 | 0.7613907 | 0.06952133 | race           | 1.1578819 | 0.1216623 | 0.9148438 |\n",
       "| priMalig | -0.2474031 | 0.7808259 | 0.09642004 | priMalig       | 1.0158327 | 0.1279569 | 0.8929364 |\n",
       "| priMalig_race | -0.2800633 | 0.7557359 | 0.06225612 | priMalig_race  | 1.2058179 | 0.1216623 | 0.9148438 |\n",
       "| treatment | -0.2464894 | 0.7815396 | 0.10053753 | treatment      | 0.9976718 | 0.1279569 | 0.8929364 |\n",
       "| treatment_race | -0.2757088 | 0.7590339 | 0.06802739 | treatment_race | 1.1673162 | 0.1216623 | 0.9148438 |\n",
       "| stage | -0.2015116 | 0.8174941 | 0.16882792 | stage          | 0.7725557 | 0.1818147 | 0.7403711 |\n",
       "| stage_race | -0.2582645 | 0.7723909 | 0.08204171 | stage_race     | 1.0859653 | 0.1276204 | 0.8940798 |\n",
       "| t | -0.2253631 | 0.7982263 | 0.11036446 | t              | 0.9571708 | 0.1287585 | 0.8902240 |\n",
       "| t_race | -0.2791573 | 0.7564209 | 0.05465295 | t_race         | 1.2623864 | 0.1216623 | 0.9148438 |\n",
       "| n | -0.2929777 | 0.7460388 | 0.02961009 | n              | 1.5285603 | 0.1216623 | 0.9148438 |\n",
       "| n_race | -0.3053085 | 0.7368960 | 0.03144463 | n_race         | 1.5024535 | 0.1216623 | 0.9148438 |\n",
       "| m | -0.2778825 | 0.7573858 | 0.05459017 | m              | 1.2628856 | 0.1216623 | 0.9148438 |\n",
       "| m_race | -0.3028784 | 0.7386889 | 0.04792513 | m_race         | 1.3194367 | 0.1216623 | 0.9148438 |\n",
       "\n",
       "\n",
       "$SLC22A15\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | 0.2688539 | 1.308464 | 0.07434015 | base           | 1.1287765 | 0.21018728 | 0.6773936 |\n",
       "| race | 0.2128963 | 1.237256 | 0.17151090 | race           | 0.7657083 | 0.21018728 | 0.6773936 |\n",
       "| priMalig | 0.2119131 | 1.236040 | 0.16761025 | priMalig       | 0.7756994 | 0.21018728 | 0.6773936 |\n",
       "| priMalig_race | 0.2110181 | 1.234935 | 0.17494071 | priMalig_race  | 0.7571091 | 0.21018728 | 0.6773936 |\n",
       "| treatment | 0.2242117 | 1.251336 | 0.14627079 | treatment      | 0.8348424 | 0.21018728 | 0.6773936 |\n",
       "| treatment_race | 0.2231841 | 1.250051 | 0.15574360 | treatment_race | 0.8075898 | 0.21018728 | 0.6773936 |\n",
       "| stage | 0.4197153 | 1.521528 | 0.00881893 | stage          | 2.0545841 | 0.06173251 | 1.2094861 |\n",
       "| stage_race | 0.4298565 | 1.537037 | 0.00791663 | stage_race     | 2.1014596 | 0.06173251 | 1.2094861 |\n",
       "| t | 0.1861073 | 1.204551 | 0.21018728 | t              | 0.6773936 | 0.21018728 | 0.6773936 |\n",
       "| t_race | 0.1975572 | 1.218423 | 0.18670321 | t_race         | 0.7288482 | 0.21018728 | 0.6773936 |\n",
       "| n | 0.2039938 | 1.226291 | 0.20274418 | n              | 0.6930516 | 0.21018728 | 0.6773936 |\n",
       "| n_race | 0.2381646 | 1.268918 | 0.14517008 | n_race         | 0.8381229 | 0.21018728 | 0.6773936 |\n",
       "| m | 0.2343360 | 1.264069 | 0.14160843 | m              | 0.8489109 | 0.21018728 | 0.6773936 |\n",
       "| m_race | 0.2446312 | 1.277150 | 0.13075190 | m_race         | 0.8835520 | 0.21018728 | 0.6773936 |\n",
       "\n",
       "\n",
       "$SLC22A11\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.3635193 | 0.6952253 | 0.03206192 | base           | 1.494010 | 0.05481908 | 1.261068 |\n",
       "| race | -0.3119401 | 0.7320254 | 0.04698779 | race           | 1.328015 | 0.05481908 | 1.261068 |\n",
       "| priMalig | -0.3122723 | 0.7317823 | 0.04485897 | priMalig       | 1.348151 | 0.05481908 | 1.261068 |\n",
       "| priMalig_race | -0.3180000 | 0.7276028 | 0.04321942 | priMalig_race  | 1.364321 | 0.05481908 | 1.261068 |\n",
       "| treatment | -0.3084579 | 0.7345788 | 0.04691170 | treatment      | 1.328719 | 0.05481908 | 1.261068 |\n",
       "| treatment_race | -0.3136502 | 0.7307746 | 0.04603630 | treatment_race | 1.336900 | 0.05481908 | 1.261068 |\n",
       "| stage | -0.3936648 | 0.6745801 | 0.02177259 | stage          | 1.662090 | 0.05481908 | 1.261068 |\n",
       "| stage_race | -0.4057206 | 0.6664963 | 0.01657187 | stage_race     | 1.780629 | 0.05481908 | 1.261068 |\n",
       "| t | -0.3516416 | 0.7035322 | 0.04378668 | t              | 1.358658 | 0.05481908 | 1.261068 |\n",
       "| t_race | -0.3623216 | 0.6960585 | 0.03486353 | t_race         | 1.457629 | 0.05481908 | 1.261068 |\n",
       "| n | -0.3941567 | 0.6742484 | 0.01178682 | n              | 1.928603 | 0.05481908 | 1.261068 |\n",
       "| n_race | -0.4026736 | 0.6685303 | 0.01106625 | n_race         | 1.956000 | 0.05481908 | 1.261068 |\n",
       "| m | -0.2850493 | 0.7519771 | 0.06356282 | m              | 1.196797 | 0.06845226 | 1.164612 |\n",
       "| m_race | -0.2825700 | 0.7538438 | 0.06850734 | m_race         | 1.164263 | 0.06850734 | 1.164263 |\n",
       "\n",
       "\n",
       "$SLC22A23\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | 0.4306382 | 1.538239 | 0.014916256 | base           | 1.826340 | 0.02610345 | 1.583302 |\n",
       "| race | 0.4202414 | 1.522329 | 0.010141267 | race           | 1.993908 | 0.02059789 | 1.686177 |\n",
       "| priMalig | 0.4154642 | 1.515074 | 0.010298946 | priMalig       | 1.987207 | 0.02059789 | 1.686177 |\n",
       "| priMalig_race | 0.4219007 | 1.524857 | 0.009939883 | priMalig_race  | 2.002619 | 0.02059789 | 1.686177 |\n",
       "| treatment | 0.4147639 | 1.514013 | 0.010234579 | treatment      | 1.989930 | 0.02059789 | 1.686177 |\n",
       "| treatment_race | 0.4222213 | 1.525346 | 0.009751555 | treatment_race | 2.010926 | 0.02059789 | 1.686177 |\n",
       "| stage | 0.4879161 | 1.628918 | 0.003616781 | stage          | 2.441678 | 0.02059789 | 1.686177 |\n",
       "| stage_race | 0.5033201 | 1.654204 | 0.002904347 | stage_race     | 2.536951 | 0.02059789 | 1.686177 |\n",
       "| t | 0.3296883 | 1.390535 | 0.062993722 | t              | 1.200703 | 0.06849331 | 1.164352 |\n",
       "| t_race | 0.3251847 | 1.384286 | 0.068938410 | t_race         | 1.161539 | 0.06893841 | 1.161539 |\n",
       "| n | 0.3456039 | 1.412843 | 0.057023115 | n              | 1.243949 | 0.06849331 | 1.164352 |\n",
       "| n_race | 0.3424405 | 1.408381 | 0.063600927 | n_race         | 1.196537 | 0.06849331 | 1.164352 |\n",
       "| m | 0.3571492 | 1.429249 | 0.042185009 | m              | 1.374842 | 0.05905901 | 1.228714 |\n",
       "| m_race | 0.3671640 | 1.443635 | 0.037741551 | m_race         | 1.423180 | 0.05870908 | 1.231295 |\n",
       "\n",
       "\n",
       "$SLC22A12\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.4526312 | 0.6359526 | 0.010797048 | base           | 1.966695 | 0.02797277 | 1.553265 |\n",
       "| race | -0.3617442 | 0.6964605 | 0.024944324 | race           | 1.603028 | 0.02797277 | 1.553265 |\n",
       "| priMalig | -0.3486193 | 0.7056617 | 0.025974714 | priMalig       | 1.585449 | 0.02797277 | 1.553265 |\n",
       "| priMalig_race | -0.3677997 | 0.6922558 | 0.022361922 | priMalig_race  | 1.650491 | 0.02797277 | 1.553265 |\n",
       "| treatment | -0.3441197 | 0.7088441 | 0.028489127 | treatment      | 1.545321 | 0.02848913 | 1.545321 |\n",
       "| treatment_race | -0.3612062 | 0.6968353 | 0.025152561 | treatment_race | 1.599418 | 0.02797277 | 1.553265 |\n",
       "| stage | -0.3945856 | 0.6739593 | 0.024839065 | stage          | 1.604865 | 0.02797277 | 1.553265 |\n",
       "| stage_race | -0.4353729 | 0.6470234 | 0.015185631 | stage_race     | 1.818567 | 0.02797277 | 1.553265 |\n",
       "| t | -0.4476685 | 0.6391165 | 0.014061958 | t              | 1.851954 | 0.02797277 | 1.553265 |\n",
       "| t_race | -0.4686108 | 0.6258712 | 0.011283793 | t_race         | 1.947545 | 0.02797277 | 1.553265 |\n",
       "| n | -0.4513201 | 0.6367870 | 0.004655780 | n              | 2.332008 | 0.02797277 | 1.553265 |\n",
       "| n_race | -0.4526342 | 0.6359507 | 0.005398379 | n_race         | 2.267737 | 0.02797277 | 1.553265 |\n",
       "| m | -0.3847837 | 0.6805978 | 0.016652670 | m              | 1.778516 | 0.02797277 | 1.553265 |\n",
       "| m_race | -0.3973236 | 0.6721165 | 0.015958008 | m_race         | 1.797021 | 0.02797277 | 1.553265 |\n",
       "\n",
       "\n",
       "$SLC22A24\n",
       ":   \n",
       "A data.frame: 14 × 7\n",
       "\n",
       "| <!--/--> | coef &lt;dbl&gt; | hazard &lt;dbl&gt; | pval &lt;dbl&gt; | comp &lt;chr&gt; | negLog.pval &lt;dbl&gt; | padj &lt;dbl&gt; | negLog.padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| base | -0.6141615 | 0.5410944 | 7.279835e-04 | base           | 3.137878 | 0.0010352591 | 2.984951 |\n",
       "| race | -0.6056222 | 0.5457347 | 5.743739e-04 | race           | 3.240805 | 0.0010352591 | 2.984951 |\n",
       "| priMalig | -0.5916532 | 0.5534116 | 6.834294e-04 | priMalig       | 3.165306 | 0.0010352591 | 2.984951 |\n",
       "| priMalig_race | -0.6179197 | 0.5390647 | 5.080191e-04 | priMalig_race  | 3.294120 | 0.0010352591 | 2.984951 |\n",
       "| treatment | -0.5801044 | 0.5598399 | 7.394708e-04 | treatment      | 3.131079 | 0.0010352591 | 2.984951 |\n",
       "| treatment_race | -0.6077782 | 0.5445594 | 5.399233e-04 | treatment_race | 3.267668 | 0.0010352591 | 2.984951 |\n",
       "| stage | -0.7178532 | 0.4877984 | 4.989184e-05 | stage          | 4.301970 | 0.0003492429 | 3.456872 |\n",
       "| stage_race | -0.7573798 | 0.4688934 | 3.263103e-05 | stage_race     | 4.486369 | 0.0003492429 | 3.456872 |\n",
       "| t | -0.5256927 | 0.5911457 | 3.794892e-03 | t              | 2.420801 | 0.0037948915 | 2.420801 |\n",
       "| t_race | -0.5413052 | 0.5819881 | 3.251946e-03 | t_race         | 2.487857 | 0.0035020960 | 2.455672 |\n",
       "| n | -0.6398630 | 0.5273647 | 3.230589e-04 | n              | 3.490718 | 0.0010352591 | 2.984951 |\n",
       "| n_race | -0.7126921 | 0.4903224 | 9.898186e-05 | n_race         | 4.004444 | 0.0004619153 | 3.335438 |\n",
       "| m | -0.5309554 | 0.5880429 | 1.925392e-03 | m              | 2.715481 | 0.0022462908 | 2.648534 |\n",
       "| m_race | -0.5649519 | 0.5683875 | 1.263062e-03 | m_race         | 2.898575 | 0.0016075339 | 2.793840 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$SLC22A18\n",
       "                     coef    hazard         pval           comp negLog.pval\n",
       "base           -0.5529791 0.5752336 4.015022e-04           base    3.396312\n",
       "race           -0.4555792 0.6340806 5.443624e-03           race    2.264112\n",
       "priMalig       -0.4597203 0.6314602 5.643067e-03       priMalig    2.248485\n",
       "priMalig_race  -0.4671379 0.6267937 4.542739e-03  priMalig_race    2.342682\n",
       "treatment      -0.4485935 0.6385256 6.722625e-03      treatment    2.172461\n",
       "treatment_race -0.4551692 0.6343406 5.563081e-03 treatment_race    2.254685\n",
       "stage          -0.5224717 0.5930529 5.201296e-04          stage    3.283888\n",
       "stage_race     -0.5370928 0.5844449 3.296065e-04     stage_race    3.482004\n",
       "t              -0.6267465 0.5343274 1.386588e-04              t    3.858053\n",
       "t_race         -0.6503880 0.5218433 7.700767e-05         t_race    4.113466\n",
       "n              -0.4233537 0.6548469 4.340832e-03              n    2.362427\n",
       "n_race         -0.4245808 0.6540439 4.014569e-03         n_race    2.396361\n",
       "m              -0.4336793 0.6481201 9.557998e-03              m    2.019633\n",
       "m_race         -0.4434774 0.6418007 8.342631e-03         m_race    2.078697\n",
       "                       padj negLog.padj\n",
       "base           0.0014052576    2.852244\n",
       "race           0.0071820852    2.143749\n",
       "priMalig       0.0071820852    2.143749\n",
       "priMalig_race  0.0071820852    2.143749\n",
       "treatment      0.0078430627    2.105514\n",
       "treatment_race 0.0071820852    2.143749\n",
       "stage          0.0014563628    2.836730\n",
       "stage_race     0.0014052576    2.852244\n",
       "t              0.0009706116    3.012955\n",
       "t_race         0.0009706116    3.012955\n",
       "n              0.0071820852    2.143749\n",
       "n_race         0.0071820852    2.143749\n",
       "m              0.0095579976    2.019633\n",
       "m_race         0.0089843718    2.046512\n",
       "\n",
       "$SLC22A1\n",
       "                     coef   hazard      pval           comp negLog.pval\n",
       "base           0.13474132 1.144241 0.3288761           base  0.48296774\n",
       "race           0.10537154 1.111123 0.4804395           race  0.31836134\n",
       "priMalig       0.10310895 1.108612 0.4824802       priMalig  0.31652055\n",
       "priMalig_race  0.10249956 1.107937 0.4926811  priMalig_race  0.30743406\n",
       "treatment      0.11160181 1.118068 0.4493267      treatment  0.34743780\n",
       "treatment_race 0.11150145 1.117955 0.4579416 treatment_race  0.33918989\n",
       "stage          0.25554519 1.291165 0.1033279          stage  0.98578258\n",
       "stage_race     0.25689877 1.292914 0.1063429     stage_race  0.97329144\n",
       "t              0.00388421 1.003892 0.9769607              t  0.01012292\n",
       "t_race         0.01717292 1.017321 0.9004880         t_race  0.04552209\n",
       "n              0.08478981 1.088488 0.5507965              n  0.25900882\n",
       "n_race         0.07472680 1.077590 0.6070861         n_race  0.21674969\n",
       "m              0.07591994 1.078876 0.6171934              m  0.20957870\n",
       "m_race         0.06555997 1.067757 0.6703707         m_race  0.17368499\n",
       "                    padj negLog.padj\n",
       "base           0.7820991  0.10673820\n",
       "race           0.7820991  0.10673820\n",
       "priMalig       0.7820991  0.10673820\n",
       "priMalig_race  0.7820991  0.10673820\n",
       "treatment      0.7820991  0.10673820\n",
       "treatment_race 0.7820991  0.10673820\n",
       "stage          0.7444004  0.12819340\n",
       "stage_race     0.7444004  0.12819340\n",
       "t              0.9769607  0.01012292\n",
       "t_race         0.9697563  0.01333740\n",
       "n              0.7820991  0.10673820\n",
       "n_race         0.7820991  0.10673820\n",
       "m              0.7820991  0.10673820\n",
       "m_race         0.7820991  0.10673820\n",
       "\n",
       "$SLC22A3\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.1576155 0.8541781 0.38488802           base   0.4146656\n",
       "race           -0.3487025 0.7056030 0.02978642           race   1.5259816\n",
       "priMalig       -0.3618447 0.6963905 0.02634301       priMalig   1.5793345\n",
       "priMalig_race  -0.3480427 0.7060687 0.03136491  priMalig_race   1.5035559\n",
       "treatment      -0.3720835 0.6892967 0.02353388      treatment   1.6283064\n",
       "treatment_race -0.3585744 0.6986716 0.02775584 treatment_race   1.5566456\n",
       "stage          -0.1356536 0.8731450 0.46334024          stage   0.3341000\n",
       "stage_race     -0.1379468 0.8711451 0.45206244     stage_race   0.3448016\n",
       "t              -0.2888458 0.7491277 0.09247548              t   1.0339734\n",
       "t_race         -0.2758034 0.7589621 0.11050628         t_race   0.9566130\n",
       "n              -0.1865966 0.8297784 0.28685219              n   0.5423418\n",
       "n_race         -0.1714353 0.8424548 0.32561348         n_race   0.4872976\n",
       "m              -0.3065829 0.7359575 0.07736576              m   1.1114512\n",
       "m_race         -0.2903929 0.7479696 0.08996423         m_race   1.0459301\n",
       "                     padj negLog.padj\n",
       "base           0.44903602   0.3477188\n",
       "race           0.08782176   1.0563979\n",
       "priMalig       0.08782176   1.0563979\n",
       "priMalig_race  0.08782176   1.0563979\n",
       "treatment      0.08782176   1.0563979\n",
       "treatment_race 0.08782176   1.0563979\n",
       "stage          0.46334024   0.3341000\n",
       "stage_race     0.46334024   0.3341000\n",
       "t              0.16183209   0.7909354\n",
       "t_race         0.17189866   0.7647275\n",
       "n              0.40159307   0.3962138\n",
       "n_race         0.41441715   0.3825623\n",
       "m              0.16183209   0.7909354\n",
       "m_race         0.16183209   0.7909354\n",
       "\n",
       "$SLC22A2\n",
       "                     coef    hazard         pval           comp negLog.pval\n",
       "base           -0.7817740 0.4575935 3.550625e-05           base    4.449695\n",
       "race           -0.8852318 0.4126185 2.829581e-06           race    5.548278\n",
       "priMalig       -0.9105052 0.4023209 1.096492e-06       priMalig    5.959994\n",
       "priMalig_race  -0.8863224 0.4121688 2.294701e-06  priMalig_race    5.639274\n",
       "treatment      -0.9087330 0.4030346 1.376105e-06      treatment    5.861348\n",
       "treatment_race -0.8841183 0.4130782 2.876815e-06 treatment_race    5.541088\n",
       "stage          -0.8608859 0.4227874 1.695237e-06          stage    5.770770\n",
       "stage_race     -0.8513621 0.4268331 1.434635e-06     stage_race    5.843259\n",
       "t              -0.8081409 0.4456859 1.141854e-05              t    4.942389\n",
       "t_race         -0.7879744 0.4547650 1.409344e-05         t_race    4.850983\n",
       "n              -0.7515369 0.4716412 3.471360e-05              n    4.459500\n",
       "n_race         -0.7455377 0.4744791 3.207595e-05         n_race    4.493820\n",
       "m              -0.8844331 0.4129482 1.168860e-05              m    4.932238\n",
       "m_race         -0.8445918 0.4297328 2.523022e-05         m_race    4.598079\n",
       "                       padj negLog.padj\n",
       "base           3.550625e-05    4.449695\n",
       "race           5.753629e-06    5.240058\n",
       "priMalig       5.753629e-06    5.240058\n",
       "priMalig_race  5.753629e-06    5.240058\n",
       "treatment      5.753629e-06    5.240058\n",
       "treatment_race 5.753629e-06    5.240058\n",
       "stage          5.753629e-06    5.240058\n",
       "stage_race     5.753629e-06    5.240058\n",
       "t              1.818226e-05    4.740352\n",
       "t_race         1.973082e-05    4.704855\n",
       "n              3.550625e-05    4.449695\n",
       "n_race         3.550625e-05    4.449695\n",
       "m              1.818226e-05    4.740352\n",
       "m_race         3.211119e-05    4.493344\n",
       "\n",
       "$SLC22A4\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.2961243 0.7436950 0.08900649           base   1.0505783\n",
       "race           -0.2962588 0.7435950 0.10163499           race   0.9929568\n",
       "priMalig       -0.3040131 0.7378512 0.08917201       priMalig   1.0497715\n",
       "priMalig_race  -0.2933213 0.7457825 0.10739933  priMalig_race   0.9689984\n",
       "treatment      -0.3168036 0.7284738 0.07605500      treatment   1.1188722\n",
       "treatment_race -0.3057072 0.7366022 0.09262746 treatment_race   1.0332602\n",
       "stage          -0.2788685 0.7566394 0.09631543          stage   1.0163041\n",
       "stage_race     -0.2777735 0.7574684 0.10214153     stage_race   0.9907977\n",
       "t              -0.2870936 0.7504414 0.09764978              t   1.0103287\n",
       "t_race         -0.2771767 0.7579206 0.11387763         t_race   0.9435616\n",
       "n              -0.3557770 0.7006288 0.04814394              n   1.3174584\n",
       "n_race         -0.3645341 0.6945201 0.04581471         n_race   1.3389950\n",
       "m              -0.3292454 0.7194664 0.05893603              m   1.2296191\n",
       "m_race         -0.3034125 0.7382945 0.08674550         m_race   1.0617531\n",
       "                    padj negLog.padj\n",
       "base           0.1138776   0.9435616\n",
       "race           0.1138776   0.9435616\n",
       "priMalig       0.1138776   0.9435616\n",
       "priMalig_race  0.1138776   0.9435616\n",
       "treatment      0.1138776   0.9435616\n",
       "treatment_race 0.1138776   0.9435616\n",
       "stage          0.1138776   0.9435616\n",
       "stage_race     0.1138776   0.9435616\n",
       "t              0.1138776   0.9435616\n",
       "t_race         0.1138776   0.9435616\n",
       "n              0.1138776   0.9435616\n",
       "n_race         0.1138776   0.9435616\n",
       "m              0.1138776   0.9435616\n",
       "m_race         0.1138776   0.9435616\n",
       "\n",
       "$SLC22A5\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.3214742 0.7250794 0.12147231           base   0.9155227\n",
       "race           -0.4119284 0.6623717 0.02639488           race   1.5784803\n",
       "priMalig       -0.4032794 0.6681254 0.02769500       priMalig   1.5575987\n",
       "priMalig_race  -0.4192504 0.6575396 0.02450147  priMalig_race   1.6108078\n",
       "treatment      -0.4018718 0.6690665 0.02811151      treatment   1.5511159\n",
       "treatment_race -0.4166937 0.6592228 0.02515555 treatment_race   1.5993662\n",
       "stage          -0.2203511 0.8022371 0.28074113          stage   0.5516939\n",
       "stage_race     -0.2294049 0.7950065 0.26790881     stage_race   0.5720130\n",
       "t              -0.3050243 0.7371055 0.13728672              t   0.8623715\n",
       "t_race         -0.3078603 0.7350180 0.13779017         t_race   0.8607818\n",
       "n              -0.3794710 0.6842233 0.05398372              n   1.2677372\n",
       "n_race         -0.3943869 0.6740932 0.04511138         n_race   1.3457139\n",
       "m              -0.3447614 0.7083894 0.04984492              m   1.3023791\n",
       "m_race         -0.3393600 0.7122260 0.05641726         m_race   1.2485880\n",
       "                     padj negLog.padj\n",
       "base           0.16075520   0.7938350\n",
       "race           0.07871222   1.1039578\n",
       "priMalig       0.07871222   1.1039578\n",
       "priMalig_race  0.07871222   1.1039578\n",
       "treatment      0.07871222   1.1039578\n",
       "treatment_race 0.07871222   1.1039578\n",
       "stage          0.28074113   0.5516939\n",
       "stage_race     0.28074113   0.5516939\n",
       "t              0.16075520   0.7938350\n",
       "t_race         0.16075520   0.7938350\n",
       "n              0.08776018   1.0567025\n",
       "n_race         0.08776018   1.0567025\n",
       "m              0.08776018   1.0567025\n",
       "m_race         0.08776018   1.0567025\n",
       "\n",
       "$SLC22A6\n",
       "                      coef    hazard      pval           comp negLog.pval\n",
       "base           -0.11754768 0.8890981 0.5191883           base  0.28467513\n",
       "race           -0.04552751 0.9554933 0.7938364           race  0.10026898\n",
       "priMalig       -0.04464710 0.9563349 0.7949461       priMalig  0.09966232\n",
       "priMalig_race  -0.04448794 0.9564871 0.7976486  priMalig_race  0.09818841\n",
       "treatment      -0.04835582 0.9527947 0.7789184      treatment  0.10850805\n",
       "treatment_race -0.04722964 0.9538683 0.7863848 treatment_race  0.10436486\n",
       "stage          -0.04675201 0.9543240 0.8019877          stage  0.09583231\n",
       "stage_race     -0.05053270 0.9507228 0.7868357     stage_race  0.10411595\n",
       "t              -0.21170254 0.8092054 0.2615762              t  0.58240184\n",
       "t_race         -0.21568404 0.8059899 0.2536680         t_race  0.59573423\n",
       "n              -0.15067649 0.8601259 0.3909433              n  0.40788624\n",
       "n_race         -0.16811617 0.8452556 0.3442363         n_race  0.46314330\n",
       "m              -0.15625882 0.8553378 0.3884121              m  0.41070730\n",
       "m_race         -0.15169123 0.8592536 0.4066059         m_race  0.39082635\n",
       "                    padj negLog.padj\n",
       "base           0.8019877  0.09583231\n",
       "race           0.8019877  0.09583231\n",
       "priMalig       0.8019877  0.09583231\n",
       "priMalig_race  0.8019877  0.09583231\n",
       "treatment      0.8019877  0.09583231\n",
       "treatment_race 0.8019877  0.09583231\n",
       "stage          0.8019877  0.09583231\n",
       "stage_race     0.8019877  0.09583231\n",
       "t              0.8019877  0.09583231\n",
       "t_race         0.8019877  0.09583231\n",
       "n              0.8019877  0.09583231\n",
       "n_race         0.8019877  0.09583231\n",
       "m              0.8019877  0.09583231\n",
       "m_race         0.8019877  0.09583231\n",
       "\n",
       "$SLC22A8\n",
       "                      coef    hazard      pval           comp negLog.pval\n",
       "base           -0.08466804 0.9188172 0.6434806           base  0.19146457\n",
       "race            0.09806979 1.1030398 0.5506169           race  0.25915045\n",
       "priMalig        0.10093490 1.1062046 0.5367857       priMalig  0.27019905\n",
       "priMalig_race   0.09602066 1.1007818 0.5586265  priMalig_race  0.25287845\n",
       "treatment       0.10100838 1.1062859 0.5357554      treatment  0.27103348\n",
       "treatment_race  0.09713791 1.1020123 0.5538041 treatment_race  0.25664386\n",
       "stage          -0.01958032 0.9806101 0.9152165          stage  0.03847617\n",
       "stage_race     -0.02761548 0.9727623 0.8801478     stage_race  0.05544441\n",
       "t              -0.03636214 0.9642910 0.8462808              t  0.07248551\n",
       "t_race         -0.04350308 0.9574296 0.8170881         t_race  0.08773110\n",
       "n              -0.01612031 0.9840089 0.9240376              n  0.03431035\n",
       "n_race         -0.03273859 0.9677915 0.8491155         n_race  0.07103325\n",
       "m              -0.06656792 0.9355994 0.7062399              m  0.15104774\n",
       "m_race         -0.08260712 0.9207128 0.6461885         m_race  0.18964080\n",
       "                    padj negLog.padj\n",
       "base           0.9240376  0.03431035\n",
       "race           0.9240376  0.03431035\n",
       "priMalig       0.9240376  0.03431035\n",
       "priMalig_race  0.9240376  0.03431035\n",
       "treatment      0.9240376  0.03431035\n",
       "treatment_race 0.9240376  0.03431035\n",
       "stage          0.9240376  0.03431035\n",
       "stage_race     0.9240376  0.03431035\n",
       "t              0.9240376  0.03431035\n",
       "t_race         0.9240376  0.03431035\n",
       "n              0.9240376  0.03431035\n",
       "n_race         0.9240376  0.03431035\n",
       "m              0.9240376  0.03431035\n",
       "m_race         0.9240376  0.03431035\n",
       "\n",
       "$SLC22A14\n",
       "                      coef    hazard       pval           comp negLog.pval\n",
       "base           -0.11599982 0.8904754 0.49459540           base   0.3057499\n",
       "race           -0.23182824 0.7930823 0.16319892           race   0.7872827\n",
       "priMalig       -0.22081631 0.8018640 0.18198340       priMalig   0.7399682\n",
       "priMalig_race  -0.23473619 0.7907794 0.15651092  priMalig_race   0.8054554\n",
       "treatment      -0.21734938 0.8046488 0.19215218      treatment   0.7163547\n",
       "treatment_race -0.23094084 0.7937864 0.16605555 treatment_race   0.7797466\n",
       "stage          -0.05644030 0.9451229 0.72883238          stage   0.1373723\n",
       "stage_race     -0.06691126 0.9352782 0.68624395     stage_race   0.1635215\n",
       "t              -0.19703065 0.8211655 0.21827773              t   0.6609906\n",
       "t_race         -0.19785233 0.8204910 0.22217356         t_race   0.6533076\n",
       "n              -0.30141010 0.7397743 0.07757237              n   1.1102929\n",
       "n_race         -0.28448587 0.7524010 0.09836765         n_race   1.0071477\n",
       "m              -0.29020266 0.7481119 0.08973468              m   1.0470397\n",
       "m_race         -0.31136372 0.7324474 0.06869615         m_race   1.1630676\n",
       "                    padj negLog.padj\n",
       "base           0.5770280   0.2388031\n",
       "race           0.2827663   0.5485723\n",
       "priMalig       0.2827663   0.5485723\n",
       "priMalig_race  0.2827663   0.5485723\n",
       "treatment      0.2827663   0.5485723\n",
       "treatment_race 0.2827663   0.5485723\n",
       "stage          0.7288324   0.1373723\n",
       "stage_race     0.7288324   0.1373723\n",
       "t              0.2827663   0.5485723\n",
       "t_race         0.2827663   0.5485723\n",
       "n              0.2827663   0.5485723\n",
       "n_race         0.2827663   0.5485723\n",
       "m              0.2827663   0.5485723\n",
       "m_race         0.2827663   0.5485723\n",
       "\n",
       "$SLC22A13\n",
       "                     coef    hazard         pval           comp negLog.pval\n",
       "base           -0.3435566 0.7092433 0.0268222290           base    1.571505\n",
       "race           -0.4244354 0.6541390 0.0078498277           race    2.105140\n",
       "priMalig       -0.4003243 0.6701027 0.0117622811       priMalig    1.929508\n",
       "priMalig_race  -0.4317372 0.6493800 0.0069765180  priMalig_race    2.156361\n",
       "treatment      -0.3997782 0.6704687 0.0122225943      treatment    1.912837\n",
       "treatment_race -0.4282103 0.6516744 0.0076029911 treatment_race    2.119016\n",
       "stage          -0.2792254 0.7563694 0.0795272728          stage    1.099484\n",
       "stage_race     -0.3282254 0.7202007 0.0420689445     stage_race    1.376038\n",
       "t              -0.3998200 0.6704407 0.0081350474              t    2.089640\n",
       "t_race         -0.4324787 0.6488987 0.0047037037         t_race    2.327560\n",
       "n              -0.6066311 0.5451845 0.0002719233              n    3.565554\n",
       "n_race         -0.6220821 0.5368255 0.0001913499         n_race    3.718172\n",
       "m              -0.4396106 0.6442873 0.0063089152              m    2.200045\n",
       "m_race         -0.4638319 0.6288693 0.0043320615         m_race    2.363305\n",
       "                      padj negLog.padj\n",
       "base           0.031292600    1.504558\n",
       "race           0.012654518    1.897754\n",
       "priMalig       0.015556029    1.808101\n",
       "priMalig_race  0.012654518    1.897754\n",
       "treatment      0.015556029    1.808101\n",
       "treatment_race 0.012654518    1.897754\n",
       "stage          0.079527273    1.099484\n",
       "stage_race     0.045305017    1.343854\n",
       "t              0.012654518    1.897754\n",
       "t_race         0.012654518    1.897754\n",
       "n              0.001903463    2.720456\n",
       "n_race         0.001903463    2.720456\n",
       "m              0.012654518    1.897754\n",
       "m_race         0.012654518    1.897754\n",
       "\n",
       "$SLC22A7\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.3106495 0.7329708 0.09701397           base   1.0131657\n",
       "race           -0.3460993 0.7074423 0.04675172           race   1.3302024\n",
       "priMalig       -0.3158003 0.7292050 0.06267052       priMalig   1.2029367\n",
       "priMalig_race  -0.3451365 0.7081237 0.04743595  priMalig_race   1.3238924\n",
       "treatment      -0.3175991 0.7278946 0.06052995      treatment   1.2180297\n",
       "treatment_race -0.3480718 0.7060482 0.04554466 treatment_race   1.3415625\n",
       "stage          -0.2092262 0.8112117 0.27034234          stage   0.5680859\n",
       "stage_race     -0.2551871 0.7747715 0.19359950     stage_race   0.7130958\n",
       "t              -0.2994011 0.7412621 0.11458166              t   0.9408849\n",
       "t_race         -0.3196238 0.7264222 0.09973896         t_race   1.0011352\n",
       "n              -0.4011960 0.6695188 0.02613977              n   1.5826982\n",
       "n_race         -0.4223577 0.6554995 0.02070923         n_race   1.6838360\n",
       "m              -0.3012729 0.7398759 0.08302095              m   1.0808123\n",
       "m_race         -0.3292805 0.7194412 0.06255784         m_race   1.2037182\n",
       "                    padj negLog.padj\n",
       "base           0.1269405   0.8963998\n",
       "race           0.1096734   0.9598987\n",
       "priMalig       0.1096734   0.9598987\n",
       "priMalig_race  0.1096734   0.9598987\n",
       "treatment      0.1096734   0.9598987\n",
       "treatment_race 0.1096734   0.9598987\n",
       "stage          0.2703423   0.5680859\n",
       "stage_race     0.2084918   0.6809111\n",
       "t              0.1336786   0.8739381\n",
       "t_race         0.1269405   0.8963998\n",
       "n              0.1096734   0.9598987\n",
       "n_race         0.1096734   0.9598987\n",
       "m              0.1269405   0.8963998\n",
       "m_race         0.1096734   0.9598987\n",
       "\n",
       "$SLC22A17\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.1349697 0.8737424 0.35969783           base   0.4440622\n",
       "race           -0.2726086 0.7613907 0.06952133           race   1.1578819\n",
       "priMalig       -0.2474031 0.7808259 0.09642004       priMalig   1.0158327\n",
       "priMalig_race  -0.2800633 0.7557359 0.06225612  priMalig_race   1.2058179\n",
       "treatment      -0.2464894 0.7815396 0.10053753      treatment   0.9976718\n",
       "treatment_race -0.2757088 0.7590339 0.06802739 treatment_race   1.1673162\n",
       "stage          -0.2015116 0.8174941 0.16882792          stage   0.7725557\n",
       "stage_race     -0.2582645 0.7723909 0.08204171     stage_race   1.0859653\n",
       "t              -0.2253631 0.7982263 0.11036446              t   0.9571708\n",
       "t_race         -0.2791573 0.7564209 0.05465295         t_race   1.2623864\n",
       "n              -0.2929777 0.7460388 0.02961009              n   1.5285603\n",
       "n_race         -0.3053085 0.7368960 0.03144463         n_race   1.5024535\n",
       "m              -0.2778825 0.7573858 0.05459017              m   1.2628856\n",
       "m_race         -0.3028784 0.7386889 0.04792513         m_race   1.3194367\n",
       "                    padj negLog.padj\n",
       "base           0.3596978   0.4440622\n",
       "race           0.1216623   0.9148438\n",
       "priMalig       0.1279569   0.8929364\n",
       "priMalig_race  0.1216623   0.9148438\n",
       "treatment      0.1279569   0.8929364\n",
       "treatment_race 0.1216623   0.9148438\n",
       "stage          0.1818147   0.7403711\n",
       "stage_race     0.1276204   0.8940798\n",
       "t              0.1287585   0.8902240\n",
       "t_race         0.1216623   0.9148438\n",
       "n              0.1216623   0.9148438\n",
       "n_race         0.1216623   0.9148438\n",
       "m              0.1216623   0.9148438\n",
       "m_race         0.1216623   0.9148438\n",
       "\n",
       "$SLC22A15\n",
       "                    coef   hazard       pval           comp negLog.pval\n",
       "base           0.2688539 1.308464 0.07434015           base   1.1287765\n",
       "race           0.2128963 1.237256 0.17151090           race   0.7657083\n",
       "priMalig       0.2119131 1.236040 0.16761025       priMalig   0.7756994\n",
       "priMalig_race  0.2110181 1.234935 0.17494071  priMalig_race   0.7571091\n",
       "treatment      0.2242117 1.251336 0.14627079      treatment   0.8348424\n",
       "treatment_race 0.2231841 1.250051 0.15574360 treatment_race   0.8075898\n",
       "stage          0.4197153 1.521528 0.00881893          stage   2.0545841\n",
       "stage_race     0.4298565 1.537037 0.00791663     stage_race   2.1014596\n",
       "t              0.1861073 1.204551 0.21018728              t   0.6773936\n",
       "t_race         0.1975572 1.218423 0.18670321         t_race   0.7288482\n",
       "n              0.2039938 1.226291 0.20274418              n   0.6930516\n",
       "n_race         0.2381646 1.268918 0.14517008         n_race   0.8381229\n",
       "m              0.2343360 1.264069 0.14160843              m   0.8489109\n",
       "m_race         0.2446312 1.277150 0.13075190         m_race   0.8835520\n",
       "                     padj negLog.padj\n",
       "base           0.21018728   0.6773936\n",
       "race           0.21018728   0.6773936\n",
       "priMalig       0.21018728   0.6773936\n",
       "priMalig_race  0.21018728   0.6773936\n",
       "treatment      0.21018728   0.6773936\n",
       "treatment_race 0.21018728   0.6773936\n",
       "stage          0.06173251   1.2094861\n",
       "stage_race     0.06173251   1.2094861\n",
       "t              0.21018728   0.6773936\n",
       "t_race         0.21018728   0.6773936\n",
       "n              0.21018728   0.6773936\n",
       "n_race         0.21018728   0.6773936\n",
       "m              0.21018728   0.6773936\n",
       "m_race         0.21018728   0.6773936\n",
       "\n",
       "$SLC22A11\n",
       "                     coef    hazard       pval           comp negLog.pval\n",
       "base           -0.3635193 0.6952253 0.03206192           base    1.494010\n",
       "race           -0.3119401 0.7320254 0.04698779           race    1.328015\n",
       "priMalig       -0.3122723 0.7317823 0.04485897       priMalig    1.348151\n",
       "priMalig_race  -0.3180000 0.7276028 0.04321942  priMalig_race    1.364321\n",
       "treatment      -0.3084579 0.7345788 0.04691170      treatment    1.328719\n",
       "treatment_race -0.3136502 0.7307746 0.04603630 treatment_race    1.336900\n",
       "stage          -0.3936648 0.6745801 0.02177259          stage    1.662090\n",
       "stage_race     -0.4057206 0.6664963 0.01657187     stage_race    1.780629\n",
       "t              -0.3516416 0.7035322 0.04378668              t    1.358658\n",
       "t_race         -0.3623216 0.6960585 0.03486353         t_race    1.457629\n",
       "n              -0.3941567 0.6742484 0.01178682              n    1.928603\n",
       "n_race         -0.4026736 0.6685303 0.01106625         n_race    1.956000\n",
       "m              -0.2850493 0.7519771 0.06356282              m    1.196797\n",
       "m_race         -0.2825700 0.7538438 0.06850734         m_race    1.164263\n",
       "                     padj negLog.padj\n",
       "base           0.05481908    1.261068\n",
       "race           0.05481908    1.261068\n",
       "priMalig       0.05481908    1.261068\n",
       "priMalig_race  0.05481908    1.261068\n",
       "treatment      0.05481908    1.261068\n",
       "treatment_race 0.05481908    1.261068\n",
       "stage          0.05481908    1.261068\n",
       "stage_race     0.05481908    1.261068\n",
       "t              0.05481908    1.261068\n",
       "t_race         0.05481908    1.261068\n",
       "n              0.05481908    1.261068\n",
       "n_race         0.05481908    1.261068\n",
       "m              0.06845226    1.164612\n",
       "m_race         0.06850734    1.164263\n",
       "\n",
       "$SLC22A23\n",
       "                    coef   hazard        pval           comp negLog.pval\n",
       "base           0.4306382 1.538239 0.014916256           base    1.826340\n",
       "race           0.4202414 1.522329 0.010141267           race    1.993908\n",
       "priMalig       0.4154642 1.515074 0.010298946       priMalig    1.987207\n",
       "priMalig_race  0.4219007 1.524857 0.009939883  priMalig_race    2.002619\n",
       "treatment      0.4147639 1.514013 0.010234579      treatment    1.989930\n",
       "treatment_race 0.4222213 1.525346 0.009751555 treatment_race    2.010926\n",
       "stage          0.4879161 1.628918 0.003616781          stage    2.441678\n",
       "stage_race     0.5033201 1.654204 0.002904347     stage_race    2.536951\n",
       "t              0.3296883 1.390535 0.062993722              t    1.200703\n",
       "t_race         0.3251847 1.384286 0.068938410         t_race    1.161539\n",
       "n              0.3456039 1.412843 0.057023115              n    1.243949\n",
       "n_race         0.3424405 1.408381 0.063600927         n_race    1.196537\n",
       "m              0.3571492 1.429249 0.042185009              m    1.374842\n",
       "m_race         0.3671640 1.443635 0.037741551         m_race    1.423180\n",
       "                     padj negLog.padj\n",
       "base           0.02610345    1.583302\n",
       "race           0.02059789    1.686177\n",
       "priMalig       0.02059789    1.686177\n",
       "priMalig_race  0.02059789    1.686177\n",
       "treatment      0.02059789    1.686177\n",
       "treatment_race 0.02059789    1.686177\n",
       "stage          0.02059789    1.686177\n",
       "stage_race     0.02059789    1.686177\n",
       "t              0.06849331    1.164352\n",
       "t_race         0.06893841    1.161539\n",
       "n              0.06849331    1.164352\n",
       "n_race         0.06849331    1.164352\n",
       "m              0.05905901    1.228714\n",
       "m_race         0.05870908    1.231295\n",
       "\n",
       "$SLC22A12\n",
       "                     coef    hazard        pval           comp negLog.pval\n",
       "base           -0.4526312 0.6359526 0.010797048           base    1.966695\n",
       "race           -0.3617442 0.6964605 0.024944324           race    1.603028\n",
       "priMalig       -0.3486193 0.7056617 0.025974714       priMalig    1.585449\n",
       "priMalig_race  -0.3677997 0.6922558 0.022361922  priMalig_race    1.650491\n",
       "treatment      -0.3441197 0.7088441 0.028489127      treatment    1.545321\n",
       "treatment_race -0.3612062 0.6968353 0.025152561 treatment_race    1.599418\n",
       "stage          -0.3945856 0.6739593 0.024839065          stage    1.604865\n",
       "stage_race     -0.4353729 0.6470234 0.015185631     stage_race    1.818567\n",
       "t              -0.4476685 0.6391165 0.014061958              t    1.851954\n",
       "t_race         -0.4686108 0.6258712 0.011283793         t_race    1.947545\n",
       "n              -0.4513201 0.6367870 0.004655780              n    2.332008\n",
       "n_race         -0.4526342 0.6359507 0.005398379         n_race    2.267737\n",
       "m              -0.3847837 0.6805978 0.016652670              m    1.778516\n",
       "m_race         -0.3973236 0.6721165 0.015958008         m_race    1.797021\n",
       "                     padj negLog.padj\n",
       "base           0.02797277    1.553265\n",
       "race           0.02797277    1.553265\n",
       "priMalig       0.02797277    1.553265\n",
       "priMalig_race  0.02797277    1.553265\n",
       "treatment      0.02848913    1.545321\n",
       "treatment_race 0.02797277    1.553265\n",
       "stage          0.02797277    1.553265\n",
       "stage_race     0.02797277    1.553265\n",
       "t              0.02797277    1.553265\n",
       "t_race         0.02797277    1.553265\n",
       "n              0.02797277    1.553265\n",
       "n_race         0.02797277    1.553265\n",
       "m              0.02797277    1.553265\n",
       "m_race         0.02797277    1.553265\n",
       "\n",
       "$SLC22A24\n",
       "                     coef    hazard         pval           comp negLog.pval\n",
       "base           -0.6141615 0.5410944 7.279835e-04           base    3.137878\n",
       "race           -0.6056222 0.5457347 5.743739e-04           race    3.240805\n",
       "priMalig       -0.5916532 0.5534116 6.834294e-04       priMalig    3.165306\n",
       "priMalig_race  -0.6179197 0.5390647 5.080191e-04  priMalig_race    3.294120\n",
       "treatment      -0.5801044 0.5598399 7.394708e-04      treatment    3.131079\n",
       "treatment_race -0.6077782 0.5445594 5.399233e-04 treatment_race    3.267668\n",
       "stage          -0.7178532 0.4877984 4.989184e-05          stage    4.301970\n",
       "stage_race     -0.7573798 0.4688934 3.263103e-05     stage_race    4.486369\n",
       "t              -0.5256927 0.5911457 3.794892e-03              t    2.420801\n",
       "t_race         -0.5413052 0.5819881 3.251946e-03         t_race    2.487857\n",
       "n              -0.6398630 0.5273647 3.230589e-04              n    3.490718\n",
       "n_race         -0.7126921 0.4903224 9.898186e-05         n_race    4.004444\n",
       "m              -0.5309554 0.5880429 1.925392e-03              m    2.715481\n",
       "m_race         -0.5649519 0.5683875 1.263062e-03         m_race    2.898575\n",
       "                       padj negLog.padj\n",
       "base           0.0010352591    2.984951\n",
       "race           0.0010352591    2.984951\n",
       "priMalig       0.0010352591    2.984951\n",
       "priMalig_race  0.0010352591    2.984951\n",
       "treatment      0.0010352591    2.984951\n",
       "treatment_race 0.0010352591    2.984951\n",
       "stage          0.0003492429    3.456872\n",
       "stage_race     0.0003492429    3.456872\n",
       "t              0.0037948915    2.420801\n",
       "t_race         0.0035020960    2.455672\n",
       "n              0.0010352591    2.984951\n",
       "n_race         0.0004619153    3.335438\n",
       "m              0.0022462908    2.648534\n",
       "m_race         0.0016075339    2.793840\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KIRP_coxph.bygene <- lapply(rownames(KIRP_coxph.coef.pval_base.df), function(x) {\n",
    "    n <- do.call(rbind.data.frame, lapply(list(KIRP_coxph.coef.pval_base.df, \n",
    "                                               KIRP_coxph.coef.pval_race.df, \n",
    "                                               KIRP_coxph.coef.pval_priMalig.df, KIRP_coxph.coef.pval_priMalig_race.df, \n",
    "                                               KIRP_coxph.coef.pval_treatment.df, KIRP_coxph.coef.pval_treatment_race.df,\n",
    "                                               KIRP_coxph.coef.pval_stage.df, KIRP_coxph.coef.pval_stage_race.df, \n",
    "                                               KIRP_coxph.coef.pval_t.df, KIRP_coxph.coef.pval_t_race.df, \n",
    "                                               KIRP_coxph.coef.pval_n.df, KIRP_coxph.coef.pval_n_race.df,\n",
    "                                               KIRP_coxph.coef.pval_m.df, KIRP_coxph.coef.pval_m_race.df)\n",
    "                                               , function(y) {\n",
    "        as.numeric(y[rownames(y) %in% x,])\n",
    "    }))\n",
    "    colnames(n) <- c(\"coef\", \"hazard\", \"pval\")\n",
    "    rownames(n) <- n$comp <- c(\"base\", \"race\", \"priMalig\", \"priMalig_race\", \"treatment\", \"treatment_race\", \n",
    "                               \"stage\", \"stage_race\", \"t\", \"t_race\", \"n\", \"n_race\",\"m\", \"m_race\")\n",
    "    n$negLog.pval <- -log(n$pval, 10)\n",
    "    n$padj <- p.adjust(n$pval, method = \"BH\")\n",
    "    n$negLog.padj <- -log(n$padj, 10)\n",
    "    n\n",
    "})\n",
    "names(KIRP_coxph.bygene) <- rownames(KIRP_coxph.coef.pval_base.df) \n",
    "KIRP_coxph.bygene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "geographic-webster",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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VmKml4B09+T5hJrsdvH/XgwRpKuDM3V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\nek8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "KIRP_coxph.bygene.df <- do.call(rbind.data.frame, lapply(1:length(KIRP_coxph.bygene), function(x) {\n",
    "    cbind.data.frame(KIRP_coxph.bygene[[x]], rep(names(KIRP_coxph.bygene)[x], Gene=nrow(KIRP_coxph.bygene[[x]])))\n",
    "}))\n",
    "colnames(KIRP_coxph.bygene.df)[8] <- c(\"Gene\")\n",
    "KIRP_coxph.bygene.df.filt <- KIRP_coxph.bygene.df[!KIRP_coxph.bygene.df$Gene %in% c(\"SLC22A18AS\", \"SLC22A20P\"),]\n",
    "KIRP_coxph.bygene.df.filt$padj <- p.adjust(KIRP_coxph.bygene.df.filt$pval, method=\"BH\")\n",
    "KIRP_coxph.bygene.df.filt$negLog.padj <- -log(KIRP_coxph.bygene.df.filt$padj,10)\n",
    "\n",
    "KIRP_w_filt <- unique(KIRP_coxph.bygene.df.filt$Gene[KIRP_coxph.bygene.df.filt$negLog.pval > 3.6])\n",
    "KIRP_coxph.bygene.df.filt2 <- KIRP_coxph.bygene.df.filt[KIRP_coxph.bygene.df.filt$Gene %in% KIRP_w_filt,]\n",
    "KIRP_coxph.bygene.df.filt2$comp <- factor(as.character(KIRP_coxph.bygene.df.filt2$comp), levels=c(\n",
    "\"base\", \"race\", \"priMalig\", \"priMalig_race\", \"treatment\", \"treatment_race\", \"stage\", \"stage_race\", \"t\", \"t_race\", \n",
    "    \"n\", \"n_race\", \"m\", \"m_race\"))\n",
    "options(repr.plot.width=5, repr.plot.height=6)\n",
    "#pdf(file=\"KIRP_SLC22_coxph_analysis_summary_barplot_04272022.pdf\", width=10, height=8)\n",
    "ggbarplot(KIRP_coxph.bygene.df.filt2, x=\"Gene\", y=\"negLog.pval\", fill=\"comp\", xlab=\"\", ylab=\"-Log(pval)\",\n",
    "          position = position_dodge()) + theme(legend.title=element_text(size=0)) + \n",
    "          theme(axis.text.x = element_text(angle = 45, hjust = 1)) + geom_abline(slope = 0, intercept = 3.7, lwd=1.2)\n",
    "#dev.off()\n",
    "\n",
    "\n",
    "KIRP_coxph.bygene.df.filt.spl <- split(KIRP_coxph.bygene.df.filt, KIRP_coxph.bygene.df.filt$Gene)\n",
    "KIRP_coxph.bygene.df.max <- do.call(rbind.data.frame, lapply(KIRP_coxph.bygene.df.filt.spl, function(x) {\n",
    "    x[which(x$negLog.pval ==max(x$negLog.pval)),]\n",
    "}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "realistic-airplane",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .get_data(model, data = data):\n",
      "“The `data` argument is not provided. Data will be extracted from model fit.”\n",
      "Warning message in .get_data(model, data = data):\n",
      "“The `data` argument is not provided. Data will be extracted from model fit.”\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vz0OZKTxw9ixAGN/ZxhE8uhU9TPbKzp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\n+56xlzF+e6BjT3muKZb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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 300
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YVhHvRTB8QyK+YUK+MLJ7tdCCCW1zCnWHC7wfCYUyz4tRnDY06xIMcyPCAWA8TS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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KIRP - Optimal Cox PH model using SLC22A13\n",
    "KIRP_surv_inp.n_race <- subset(KIRP_surv_inp, Race != \"indig\")\n",
    "KIRP_surv_inp.n_race$Race <- droplevels(KIRP_surv_inp.n_race$Race)\n",
    "\n",
    "KIRP_coxph.out_treatment_n_race_nogene <- summary(coxph(Surv(time, status) ~age2 + gender + N0 + N1 + Race, data=KIRP_surv_inp.n_race))\n",
    "KIRP_coxph.mod_treatment_n_race_nogene <- coxph(Surv(time, status) ~age2 + gender + N0 + N1 + Race, data=KIRP_surv_inp.n_race)\n",
    "\n",
    "ggforest(KIRP_coxph.mod_treatment_n_race_nogene)\n",
    "\n",
    "# KIRP - Optimal Cox PH model using SLC22A18\n",
    "KIRP_surv_inp.t_race <- subset(KIRP_surv_inp, Race != \"indig\")\n",
    "KIRP_surv_inp.t_race$Race <- droplevels(KIRP_surv_inp.t_race$Race)\n",
    "\n",
    "KIRP_coxph.out_treatment_t_race_nogene <- summary(coxph(Surv(time, status) ~age2 + gender + T1 + T2 + T3 + Race, data=KIRP_surv_inp.t_race))\n",
    "KIRP_coxph.mod_treatment_t_race_nogene <- coxph(Surv(time, status) ~age2 + gender + T1 + T2 + T3 + Race, data=KIRP_surv_inp.t_race)\n",
    "\n",
    "ggforest(KIRP_coxph.mod_treatment_t_race_nogene)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93095c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 4.0.5 (2021-03-31)\n",
       "Platform: x86_64-conda-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 20.04.2 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /home/ubuntu/anaconda3/lib/libmkl_rt.so.1\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n",
       " [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n",
       " [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n",
       "[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  stats4    stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] gplots_3.1.1         survminer_0.4.9      survival_3.2-13     \n",
       " [4] ggpubr_0.4.0         ggplot2_3.3.5        dplyr_1.0.7         \n",
       " [7] limma_3.46.0         org.Hs.eg.db_3.12.0  AnnotationDbi_1.52.0\n",
       "[10] IRanges_2.24.1       S4Vectors_0.28.1     Biobase_2.50.0      \n",
       "[13] BiocGenerics_0.36.1 \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] tidyr_1.1.4        bit64_4.0.5        jsonlite_1.7.2     splines_4.0.5     \n",
       " [5] carData_3.0-4      gtools_3.9.2       assertthat_0.2.1   blob_1.2.2        \n",
       " [9] pillar_1.6.4       RSQLite_2.2.8      backports_1.3.0    lattice_0.20-45   \n",
       "[13] glue_1.5.0         uuid_1.0-3         digest_0.6.28      ggsignif_0.6.3    \n",
       "[17] colorspace_2.0-2   cowplot_1.1.1      htmltools_0.5.2    Matrix_1.3-4      \n",
       "[21] pkgconfig_2.0.3    broom_0.7.10       purrr_0.3.4        xtable_1.8-4      \n",
       "[25] scales_1.1.1       km.ci_0.5-2        KMsurv_0.1-5       tibble_3.1.6      \n",
       "[29] farver_2.1.0       generics_0.1.1     car_3.0-12         ellipsis_0.3.2    \n",
       "[33] cachem_1.0.6       withr_2.4.2        repr_1.1.3         magrittr_2.0.1    \n",
       "[37] crayon_1.4.2       memoise_2.0.0      evaluate_0.14      fansi_0.5.0       \n",
       "[41] rstatix_0.7.0      tools_4.0.5        data.table_1.14.2  lifecycle_1.0.1   \n",
       "[45] stringr_1.4.0      munsell_0.5.0      compiler_4.0.5     caTools_1.18.2    \n",
       "[49] rlang_0.4.12       grid_4.0.5         pbdZMQ_0.3-6       IRkernel_1.3      \n",
       "[53] bitops_1.0-7       base64enc_0.1-3    labeling_0.4.2     gtable_0.3.0      \n",
       "[57] abind_1.4-5        DBI_1.1.1          R6_2.5.1           gridExtra_2.3     \n",
       "[61] zoo_1.8-9          knitr_1.36         fastmap_1.1.0      survMisc_0.5.5    \n",
       "[65] bit_4.0.4          utf8_1.2.2         KernSmooth_2.23-20 stringi_1.7.3     \n",
       "[69] IRdisplay_1.0      Rcpp_1.0.7         vctrs_0.3.8        tidyselect_1.1.1  \n",
       "[73] xfun_0.28         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1142fbd4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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