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get_data_validation_sammut.R
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get_data_validation_sammut.R
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#### -------------------------------------------------------------------------------
#### created on 02 feb 2023, 06:08pm
#### author: dhrubas2
#### -------------------------------------------------------------------------------
.wpath. <- "/Users/dhrubas2/OneDrive - National Institutes of Health/Projects/TMEcontribution/analysis/submission/Code/analysis/"
.mpath. <- "miscellaneous/r/miscellaneous.R"
setwd(.wpath.) # current path
source(.mpath.)
fcat <- function(...) cat(paste0(glue::glue(...), "\n")) # f-string print akin to python
cat("\014") # clears console
#### -------------------------------------------------------------------------------
## load data.
data.path <- "../data/TransNEO_SammutShare/"
data.file <- c("ClinData.csv",
"Transneo.diagnostic.DNA.csv",
"transneo-diagnosis-DNAseq-mutations.RDS",
"transneo-diagnosis-RNAseq-validCounts.RDS",
"transneo-diagnosis-RNAseq-validTPM.RDS")
## feature data.
clin.feats.all <- read.table(paste0(data.path, data.file[1]), sep = ",",
header = T, stringsAsFactors = F)
dna.feats.all <- read.table(paste0(data.path, data.file[2]), sep = ",",
header = T, row.names = 1, stringsAsFactors = F)
## seq data.
dna.data.val <- readRDS(paste0(data.path, data.file[3]))
rna.data.val <- readRDS(paste0(data.path, data.file[4]))
tpm.data.val <- readRDS(paste0(data.path, data.file[5]))
## get training samples & coding genes for filtering.
annot.path <- "../data/TransNEO/use_data/"
annot.file <- c("TransNEO_SupplementaryTablesAll.xlsx",
"gene_length_ensembl.grch37.87_SRD_17Mar2022.txt")
clin.train <- openxlsx::read.xlsx(paste0(annot.path, annot.file[1]), sheet = 1,
startRow = 2, colNames = T, rowNames = T,
check.names = F)
gene.annot <- read.table(paste0(annot.path, annot.file[2]), sep = "\t",
header = T, row.names = 1, stringsAsFactors = F)
samples.train <- clin.train %>% rownames %>% as.character
gene.annot.pc <- gene.annot %>% filter(gene_biotype == "protein_coding")
#### -------------------------------------------------------------------------------
svdat <- F # set T to save data
## process validation RNA data.
fcat("\nvalidation data size: {tpm.data.val %>% nrow} x {tpm.data.val %>% ncol}")
## keep protein-coding genes only.
keep.genes <- intersect(gene.annot.pc$gene_name, tpm.data.val %>% rownames)
tpm.data.val.pc <- tpm.data.val[keep.genes, ] %>% as.matrix # allows duplicate rownames if exist
rownames(tpm.data.val.pc) <- tpm.data.val.pc %>% rownames %>% toupper
genes.dup <- rownames(tpm.data.val.pc) %>% table %>%
(function(freq) freq[freq > 1] %>% names) %>% # duplicated gene symbols
lapply(function(gn) which(rownames(tpm.data.val.pc) == gn)) %>%
Reduce(f = union) # duplicate symbol indices
tpm.data.val.pc <- tpm.data.val.pc[-genes.dup, ] %>% as.data.frame %>% # remove duplicates
rownames_to_column(var = "GENE")
fcat("kept protein-coding genes only: m = {tpm.data.val.pc %>% nrow}")
if (svdat){
fcat("\nsaving validation TPM data ({tpm.data.val.pc %>% nrow} x {tpm.data.val.pc %>% ncol})...")
out.file <- "transneo-validation-TPM-coding-genes_v2.txt"
write.table(tpm.data.val.pc, file = paste0(data.path, out.file), sep = "\t",
row.names = F, col.names = T)
fcat("done!")
}
#### -------------------------------------------------------------------------------
svdat <- F # set T to save data
## process clinical features listed below.
## Age.at.diagnosis, Histology, ER.status, HER2.status, LN.at.diagnosis,
## Grade.pre.chemotherapy, Size.at.diagnosis
## remove repeated sample "T186" that has been repeated with different treatment
## info- presumably due to some error- since Supplementary Table 5 only reports one
## single treatment regimen: "T-EC + Trastuzumab + Pertuzumab"
clin.feats.filt <- ((clin.feats.all$Trial.ID == "T186") &
(clin.feats.all$Regimen.Name != "T-EC + Trastuzumab + Pertuzumab")) %>%
(function(cond) clin.feats.all[-which(cond), ] %>% `rownames<-`(NULL)) %>%
column_to_rownames("Trial.ID")
## age, tumor grade: already integers- nothing to change.
## tumor size: replace "unevaluable" with 132mm.
## "Patients who had a clinically unevaluable tumour size was assumed to have a
## volume 10% larger than the largest present in the cohort (see methods) – I would
## suggest that you set this to 132 mm" (sammut et al.)
## 'imaging.size' is the pretherapy tumor size.
clin.feats.filt$Size.at.diagnosis <- clin.feats.filt$Imaging.size %>%
replace(list = (clin.feats.filt$Imaging.size == "Unevaluable") %>% which,
values = 132) %>% as.integer # max.tumor.size = 120
## histology: 1 if contains IDC, 0 otherwise (see methods).
clin.feats.filt$Histology.original <- clin.feats.filt$Histology # preserve the original data
clin.feats.filt$Histology <- clin.feats.filt$Histology %>%
grepl(pattern = "IDC") %>% as.integer
## ER, HER2, LN status: replace POS with +1, NEG with -1.
feats <- c("ER.status", "HER2.status", "LN.at.diagnosis")
clin.feats.filt[paste(feats, "original", sep = ".")] <- clin.feats.filt[feats] # preserve the original data
clin.feats.filt[feats] <- clin.feats.filt[feats] %>% apply(MARGIN = 2, function(x){
x %>% gsub(pattern = "POS", replacement = +1) %>%
gsub(pattern = "NEG", replacement = -1) %>% as.integer
})
## divide data into training & validation cohorts.
samples.trn <- intersect(clin.feats.filt %>% rownames, samples.train)
samples.val <- setdiff(clin.feats.filt %>% rownames, samples.trn)
clin.feats.trn <- clin.feats.filt[samples.trn, ]
clin.feats.val <- clin.feats.filt[samples.val, ]
fcat("\nclinical features for modeling:")
fcat("training size = {clin.feats.trn %>% nrow} x {clin.feats.trn %>% ncol}")
fcat("validation size = {clin.feats.val %>% nrow} x {clin.feats.val %>% ncol}")
if (svdat){
fcat("\nsaving clinical features in xlsx...")
out.file <- "transneo-diagnosis-clinical-features.xlsx"
out.data <- list("training" = clin.feats.trn %>% rownames_to_column("Trial.ID"),
"validation" = clin.feats.val %>% rownames_to_column("Trial.ID"))
WriteXlsx(out.data, file.name = paste0(data.path, out.file), verbose = T,
row.names = F, col.names = T)
fcat("done!")
}
#### -------------------------------------------------------------------------------
svdat <- F # set T to save data
## prepare DNA features.
dna.feats.all$HLA.LOH <- dna.feats.all$LOH.HLA # to be consistent with 'training_df.csv'
dna.feats.all$Expressed.NAg <- dna.feats.all$Expressed.NAg %>% is.na %>%
replace(x = dna.feats.all$Expressed.NAg, values = 0) # replace missing NAg values (n = 7) with 0
## separate for training & validation cohorts.
samples.trn <- intersect(dna.feats.all %>% rownames, samples.train)
samples.val <- setdiff(dna.feats.all %>% rownames, samples.trn)
dna.feats.trn <- dna.feats.all[samples.trn, ]
dna.feats.val <- dna.feats.all[samples.val, ]
if (svdat){
fcat("\nsaving DNA features...")
fcat("training: {dna.feats.trn %>% nrow} x {dna.feats.trn %>% ncol}")
fcat("validation: {dna.feats.val %>% nrow} x {dna.feats.val %>% ncol}")
out.file <- c("transneo-diagnosis-DNA-features.txt",
"transneo-validation-DNA-features.txt")
out.data <- list(dna.feats.trn %>% rownames_to_column("Trial.ID"),
dna.feats.val %>% rownames_to_column("Trial.ID"))
plyr::l_ply(1:2, function(nn){ # returns nothing
write.table(out.data[[nn]], file = paste0(data.path, out.file[nn]),
sep = "\t", col.names = T, row.names = F)
})
fcat("done!")
}