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new_micronutrients_LTBI_art_updated-ages_bmi18.Rmd
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new_micronutrients_LTBI_art_updated-ages_bmi18.Rmd
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---
title: "new_micronutrients_LTBI_art_bmi18"
author: "Arthur J. VanValkenburg"
date: "4/24/2019"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
theme: "flatly"
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(edgeR)
library(sva)
library(SingleCellExperiment)
library(singleCellTK)
library(DESeq2)
library(TBSignatureProfiler)
library(DT)
library(enrichR)
library(Rtsne)
library(umap)
library(ggplot2)
library(ComplexHeatmap)
library(tidyverse)
library(stringi)
library(hypeR)
library(knitr)
library(kableExtra)
library(readxl)
library(cutpointr)
library(readxl)
library(ggrepel)
})
knitr::opts_chunk$set(cache = TRUE)
knitr::opts_chunk$set(echo = FALSE)
#knitr::opts_chunk$set(dev = "svg", dpi = 300)
# library(reshape2)
# library(RColorBrewer)
# library(limma)
# library(knitr)
# library(pander)
# library(xtable)
#})
```
# Loading and processing data
```{r load data, include=FALSE}
#indata is a SingleCellExperiment object
indata <- readRDS("new_indata_SCE.rds")
# correcting colData
df <- read_xlsx("subject_coldata_cc_excel.xlsx")
df <- column_to_rownames(df, var="...1")
#
#change these to "well"
# relabel <- c( "10200350B")
#
# df[(match(relabel, df$SUBJID)), "bmi_cat2"] <- "well"
# df[(match(relabel, df$SUBJID)), "status"] <- "LTBI_well"
# change these to "mal"
# mal_list <- c("10200082A","10200111A")
# #
# df[(match(mal_list, df$SUBJID)), "bmi_cat2"] <- "mal"
# df[(match(mal_list, df$SUBJID)), "status"] <- "LTBI_mal"
# df$bmi_cat2 <- as.factor(df$bmi_cat2)
# df$status <- as.factor(df$status)
#df$age <- as.numeric(df$age)
# change colData to updated list (with ages fixed)
#colData(indata) <- DataFrame(df)
#indatalist <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(indatalist$indata2)), colData = DataFrame(indatalist$metadata_added))
#metadata_added = indatalist$metadata_added
#rm(indatalist)
## Removing Outliers
indata <- indata[,-which(colnames(indata) %in% c("10200459B","10200335A","10200332A"))]
# change ages
#colData(indata)$new_age <- df$age
# Want to have 5% present rate
indata <- indata[apply(assay(indata,"counts") != 0, 1, mean)>.2,]
## Correct some errors in metadata
colData(indata)$Tb_status[grep("074", colnames(indata))] <- "LTBI"
colData(indata)$Tb_status[grep("082", colnames(indata))] <- "LTBI"
colData(indata)$Tb_status[grep("111", colnames(indata))] <- "LTBI"
## Make CPM and log_cpm
#indata = mkAssay(indata, log = TRUE, counts_to_CPM = TRUE)
#assays(indata)
## rename certain labels
colData(indata)$Tb_status <- factor(gsub("activeTB", "TB", as.character(colData(indata)$Tb_status)))
colData(indata)$bmi_cat2 <- factor(gsub("severely malnourished", "mal", gsub("well nourished", "well", colData(indata)$bmi_cat2)))
colData(indata)$status <- factor(paste(colData(indata)$Tb_status, colData(indata)$bmi_cat2, sep="_"))
table(colData(indata)$status)
colData(indata)$bmi_cat2[grep("082", colnames(indata))] <- factor("mal")
colData(indata)$status[grep("082", colnames(indata))] <- factor("LTBI_mal")
colData(indata)$bmi_cat2[grep("111", colnames(indata))] <- factor("mal")
colData(indata)$status[grep("111", colnames(indata))] <- factor("LTBI_mal")
```
### ComBat Batch correction
```{r corrected data, message=FALSE, results='hide', include = FALSE}
## Make batch variable
batch=rep(1,ncol(indata))
batch[colnames(indata) %in% c("10200247B")]=1
batch[colnames(indata) %in% c("10200227B","10200346A","10200381A","10200308A","10200361A","10200365B","10200467B","10200341A","10200374A", "10200548B","10200343A","10200374B","10200346A","10200377A") ]=1
batch[colnames(indata)%in% c("10200070A", "10200128A","10200056A", "10200075A","10200136A", "10200105A","10200034A","10200058A", "10200007A", "10200064A","10200159A","10200077A","10200073A","10200130A","10200181A","10200059A","10200040B","10200018B","10200065B","10200003B","10200024B","10200004B","10200009B","10200001B","10200057B","10200029B","10200077B","10200035B","10200061B","10200030B","10200063B","10200034B","10200010A","10200288A","10200082A","10200074A","10200060A","10200023A","10200140A","10200111A","10200112A","10200016A","10200015A","10200087A")]=2
colData(indata)$batch <- factor(batch)
rm(batch)
modcombat <- model.matrix(~colData(indata)$bmi_cat2+colData(indata)$Tb_status)
combined_India.combatSeqCorrect <- ComBat_seq(assay(indata, "counts"),
colData(indata)$batch, group=NULL,
covar_mod=modcombat)
assay(indata, "combatseq") <- combined_India.combatSeqCorrect
assays(indata)
indata <- mkAssay(indata, input_name = "combatseq", log = TRUE)
names(assays(indata)) <- c("counts", "combatseq", "log_counts", "counts_cpm", "log_cpm")
rm(modcombat, combined_India.combatSeqCorrect)
```
### Keep only LTBI
```{r filter, include=FALSE}
indata = indata[,colData(indata)$Tb_status=="LTBI"]
dim(indata)
table(colData(indata)$bmi_cat2)
```
### Keep only protein coding genes
```{r filter-by-gene-type, include=FALSE}
#indata <- as(indata, "SingleCellExperiment")
prot_code <- indata@rowRanges@elementMetadata
prot_ <- prot_code[which(prot_code$V2 == "protein_coding"),]
prot <- as.character.Array(prot_@listData$V1)
indata = indata[indata@rowRanges@partitioning@NAMES %in% prot,]
rm(prot_, prot_code, prot)
```
# Distribution of data
## Boxplot of age/bmi_cat2
```{r, include=FALSE}
#library(bit64)
df$age <- as.numeric(df$age)
age <- as.numeric(df$age)
names(age) <- (df$SUBJID)
# change to match correct ages to colData
test <- as.data.frame(colData(indata)$SUBJID)
test$ages <- age[match(test$`colData(indata)$SUBJID`, names(age))]
colData(indata)$age <- test$ages
df <- as.data.frame(colData(indata))
boxplot(as.numeric(df$age)~df$bmi_cat2)
```
## Scatterplot of age and BMI
```{r}
#note, NA's are from active TB cases
g <- ggplot(data=df, mapping = aes(age, BMI, color=bmi_cat2)) +
geom_point()
g
```
## T-test for age differences
```{r}
mal <- filter(df, bmi_cat2=="mal")
well <- filter(df,bmi_cat2=="well")
t.test(as.numeric(mal$age),as.numeric(well$age))
```
## Subject ID, age, BMI, bmi_cat2
```{r}
truncated_df <- df[,c("SUBJID","age","BMI","bmi_cat2", "status")]
datatable(truncated_df,
options=list(scrollX=T,pageLength=50),
rownames = T)
```
### Make SCE for SCTK {.unlisted, .unnumbered}
```{r sce-for-sctk, echo=FALSE}
indata_SCE = as(indata, "SingleCellExperiment")
#singleCellTK(indata_SCE)
#saveRDS(indata_SCE, file = "indata_SCE.rds")
```
# Dimension Reduction Plots {.tabset}
## PCA {.tabset}
Using the DESeq object and PCAplot function on the ComBat corrected data
```{r PCA, fig.cap="PCA plot created with the DESeq object and PCAplot function on the Combat-Seq batch corrected data.", fig.height=4, fig.width=4, echo=FALSE}
#function to plot PCA plot of PC2 and PC3, as PC1 variance appears related to sex differences
plotARTPCA <- function (object, intgroup = "condition", ntop = 500, returnData = FALSE)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup %in% names(colData(object)))) {
stop("the argument 'intgroup' should specify columns of colData(dds)")
}
intgroup.df <- as.data.frame(colData(object)[, intgroup,
drop = FALSE])
group <- if (length(intgroup) > 1) {
factor(apply(intgroup.df, 1, paste, collapse = ":"))
}
else {
colData(object)[[intgroup]]
}
d <- data.frame(PC2 = pca$x[, 2], PC3 = pca$x[, 3], group = group,
intgroup.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[2:3]
return(d)
}
ggplot(data = d, aes_string(x = "PC2", y = "PC3",
color = "group")) + geom_point(size = 3) + xlab(paste0("PC2: ",
round(percentVar[2] * 100), "% variance")) + ylab(paste0("PC3: ",
round(percentVar[3] * 100), "% variance")) + coord_fixed()
}
indata_tmp <- SummarizedExperiment(assays=list(counts=assays(indata)$log_cpm),
colData = colData(indata))
#colData(indata_tmp)$sex <- as.factor(colData(indata_tmp)$sex)
plotARTPCA( DESeqTransform(indata_tmp), intgroup = "bmi_cat2")
#plotPCA(DESeqTransform(indata_tmp), intgroup = "sex")
```
## tSNE {.tabset}
```{r tSNE, fig.cap="tSNE_bmi_gradient_plot", fig.height=4, fig.width=4, echo=FALSE}
assay_type = "log_cpm"
set.seed(1)
tsne_out <- Rtsne(t(assay(indata,assay_type)), check_duplicates = FALSE, pca = TRUE, perplexity=10, theta=0.5, dims=2)
embedding <- as.data.frame(tsne_out$Y)
embedding$Class <- (indata$BMI )
g <- ggplot(embedding,
aes(x=V1, y=V2, color=Class), label = colnames(assay(indata,assay_type))) + geom_point(size=1.5) + xlab("T-SNE 1") + ylab("T-SNE 2") + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("TSNE Plot") + scale_color_gradient(low="blue", high="red")
plot(g)
```
```{r tSNE2, fig.cap="tSNE", fig.height=4, fig.width=4, echo=FALSE}
assay_type = "log_cpm"
set.seed(1)
tsne_out <- Rtsne(t(assay(indata,assay_type)), check_duplicates = FALSE, pca = TRUE, perplexity=10, theta=0.5, dims=2)
embedding <- as.data.frame(tsne_out$Y)
embedding$Class <- as.factor(indata$bmi_cat2 )
g <- ggplot(embedding,
aes(x=V1, y=V2, color=Class), label = colnames(assay(indata,assay_type))) + geom_point(size=1.5) + xlab("T-SNE 1") + ylab("T-SNE 2") + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("TSNE Plot")
plot(g)
```
## UMAP {.tabset}
```{r UMAP, fig.cap="UMAP", fig.height=4, fig.width=4, echo=FALSE}
assay_type = "log_cpm"
set.seed(1)
umap_out <- umap(t(assay(indata,assay_type)))
embedding <- as.data.frame(umap_out$layout)
embedding$Class <- as.factor(indata$bmi_cat2)
g <- ggplot(embedding, aes(x=V1, y=V2, color=Class, label = colnames(assay(indata,assay_type)))) + geom_point(size=1.5) + xlab("UMAP 1") + ylab("UMAP 2") + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("UMAP Plot")
plot(g)
```
## UMAP on PCA data {.tabset}
```{r UMAP-PCA, fig.cap="UMAP of PCA", fig.height=4, fig.width=4, echo=FALSE}
assay_type = "log_cpm"
#df_combat <- as.data.frame(indata_SCE@assays@data@listData[["log_cpm"]])
pca_df <- assay(indata, assay_type)
set.seed(1)
pca_out <- prcomp(t(pca_df))
#plot pca_out
umap_out <- umap(pca_out$x[,1:20])
#pca embedding for pca plot
embedding_pca <- as.data.frame(pca_out$x)
embedding_pca$Class <- as.factor(indata$bmi_cat2)
#pca only
g_pca <- ggplot(embedding_pca, aes(x=pca_out$x[,1],y=pca_out$x[,2], color=Class, label = colnames(assay(indata,assay_type)))) + geom_point(size=1.5) + xlab("PCA 1") + ylab("PCA 2") + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("PCA Plot")
#plot(g_pca)
#plotting umap of PCA object (1st 20 principal components)
embedding <- as.data.frame(umap_out$layout)
embedding$Class <- as.factor(indata$bmi_cat2)
g <- ggplot(embedding, aes(x=V1, y=V2, color=Class, label = colnames(assay(indata,assay_type)))) + geom_point(size=1.5) + xlab("UMAP 1") + ylab("UMAP 2") + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("UMAP of PCA Plot")
plot(g)
```
# Differential expression, bmi_cat2 {.tabset}
```{r diffex1}
designMat <- model.matrix(~factor(bmi_cat2) , data=colData(indata))
colnames(designMat)
colnames(designMat) <- c("Intercept", "wellnourished")
head(designMat)
fit <- lmFit(assay(indata, "log_cpm"), designMat)
# Difference in expression between malnourished individuals and well-nourished LTBI individuals, among malnourished individuals
contrast.matrixNut<- makeContrasts(wellnourished, levels = designMat)
fitNut <- contrasts.fit(fit,contrast.matrixNut)
fitNut <- eBayes(fitNut)
limmaResNut <- topTable(fitNut, adjust.method = "BH", n = Inf, sort.by = "P")
dim(limmaResNut[limmaResNut$adj.P.Val <0.05,])
table(colData(indata)$bmi_cat2)
new_hist_padjval_100breaks <- hist(limmaResNut$adj.P.Val, breaks=100)
new_hist_pval_100breaks <- hist(limmaResNut$P.Value, breaks=100)
##shows spike near 0
sig_limmaResNut <- limmaResNut[limmaResNut$adj.P.Val <0.1,]
sig_limmaResNut <- sig_limmaResNut[order(sig_limmaResNut$adj.P.Val),]
#write.csv(sig_limmaResNut, "sig_TBgenes_padj.csv")
top_500_genes <- limmaResNut[1:500,]
top_1k_genes <- limmaResNut[1:1000,]
top_3k_genes <- limmaResNut[1:1000,]
top_5k_genes_bmi_cat2 <- limmaResNut[1:5000,]
## top 50 by logfc
###top 50 by p.adj value
top_50 <- sig_limmaResNut[1:50,]
#write.csv(top_50, "top_50_by_padj.csv")
neg_25_log <- (sig_limmaResNut[sig_limmaResNut$logFC < 0,])
neg_25_log <- neg_25_log[order(neg_25_log$logFC),]
neg_25_log <- neg_25_log[1:25,]
#write.csv(neg_25_log, "neg_25_logfc.csv")
top25_lot <- sig_limmaResNut[sig_limmaResNut$logFC >= 0,]
top25_log <- top25_lot[order(-top25_lot$logFC),]
top25_log <- top25_log[1:25,]
#write.csv(top25_log, "top_25_logfc.csv")
```
There are `r nrow(sig_limmaResNut)` significant genes.
## 2 All genes datatable and heatmap {.tabset}
```{r diffex-datatable-heatmap1}
datatable(sig_limmaResNut,
options=list(scrollX=T,pageLength=20),
rownames = T)
## Make a Heatmap of top 500 genes
mat = as.matrix(assay(indata[row.names(top_500_genes),],"log_cpm"))
mat = t(scale(t(mat)))
mat = mat[,order(colData(indata)$"BMI")]
df=data.frame(Nutrition=colData(indata)$"bmi_cat2", BMI=(colData(indata)$"BMI"))
df=df[order(df$BMI),]
ha = HeatmapAnnotation(df = df, col = list(TB_status=c("mal"="Red","well"="Blue")))
Heatmap(mat,show_row_names=T,show_column_names = F, top_annotation = ha, cluster_columns = F )
```
## Volcano plot
```{r}
df <- limmaResNut
df$diffex <- "NO"
df$diffex[df$logFC> 0.1 & df$adj.P.Val<0.1] <- "UP"
df$diffex[df$logFC < -0.1 & df$adj.P.Val<0.1] <- "DN"
df$delabel <- NA
df$SYMBOL <- rownames(df)
thresh <- head(arrange(df, P.Value),20)$P.Value[20]
df$delabel[df$P.Value<=thresh] <- df$SYMBOL
# these are up or down relative to well-nourished LTBI individuals
g <- ggplot(data=df, aes(x=logFC,y=-log10(P.Value), col=diffex,label=delabel )) +
geom_point()+
theme_minimal()+
geom_text_repel()+
scale_color_manual(values=c("blue", "black", "red"))+
theme(text = element_text(size=20))
g
#down_genes <- head(arrange(df, P.Value),10)$SYMBOL
# svg("volcanoplot.svg", width=6.5, height = 4)
# g
# dev.off()
```
# SignatureProfiler {.tabset}
## TBSigProfiler TB Pathways {.tabset}
```{r, message=FALSE, results='hide'}
### make a character list of SUBJID from low BMI to Hi BMI
df <- as.data.frame(indata@colData@listData)
order_bmi <- select(df, SUBJID, BMI)
ordered_bmi <- order_bmi[order(order_bmi$BMI),]
## Add new signatures to the profiler
TBsignatures$Zhao_NANO_6 <- c("ANKRD22","ATP1A1","BLK","CCR6","DARS2","EXOC2")
TBsignatures$'CYTO6' <- c("CCL11", "IFNG", "IL15", "IL1B", "IL6", "CXCL10")
TBsignatures$Leong_RISK_29 <- c("SRBD1", "ZNF419", "SH2D1B","CTSA", "GSTA4", "AGAP9", "MOB3C", "WARS1", "LUC7L", "ZNRF1", "CIRBP", "PRSS53", "APOL6", "TCN2", "MDN1", "SNRNP70", "SLC3A1", "NAGA", "SPDYE5", "SPSB1", "CCDC14", "IL31RA", "DERA", "FUT4",
"NEIL1", "ENO3", "CCDC78", "HM13","ZNF202" )
TBsignatures$Zak_RISK_16 <- c("ANKRD22","APOL1","BATF2" ,"ETV7","FCGR1A", "FCGR1B", "GBP1","GBP2","GBP4","GBP5", "SCARF1","SEPTIN4", "SERPING1", "STAT1","TAP1","TRAFD1")
TBsignatures$Suliman_RISK_4 <- c("GAS6", "SEPTIN4", "CD1C", "BLK")
TBsignatures$'Leong_RISK_29_up' = c("SPSB1","MOB3C","FUT4","DERA","WARS","ZNRF1","SLC3A1","SRBD1",
"HM13", "CTSA","TCN2", "APOL6","NAGA","IL31RA")
TBsignatures$'Leong_RISK_29_dn' = c("SH2D1B","AGAP9","ZNF202","NEIL1","LUC7L","CCDC78","PRSS53","ENO3",
"CIRBP","SNRNP70","ZNF419","CCDC14","GSTA4","MDN1","SPDYE5")
TBsignatures$generegulators <- c("NONO", "IFNL1", "CKAP2L", "RNY3", "IFNG", "PRL", "FOXM1", "MITF", "IFNA", "GATA1", "TGM2", "TP53", "PGR", "TRPS1", "RC3H1", "IL1RN", "BTK", "IRGM")
#selected tb signatures
samp_tbsignatures <- list(TBsignatures$Suliman_RISK_4, TBsignatures$Sweeney_OD_3, TBsignatures$Leong_RISK_29, TBsignatures$Zak_RISK_16, TBsignatures$Leong_RISK_29_up, TBsignatures$Leong_RISK_29_dn, TBsignatures$Darboe_RISK_11, TBsignatures$generegulators)
names(samp_tbsignatures) <- c("Suliman_RISK_4", "Sweeney_OD_3", "Leong_RISK_29", "Zak_RISK_16", "Leong_RISK_29_up", "Leong_RISK_29_dn", "Darboe_RISK_11", "generegulators")
gsva_res <- runTBsigProfiler(indata, useAssay = "log_cpm", algorithm = "GSVA",
signatures = samp_tbsignatures, parallel.sz = 4)
ssgsea_res <- runTBsigProfiler(indata, useAssay = "log_cpm", algorithm = "ssGSEA",
signatures = samp_tbsignatures, parallel.sz = 4)
plage_res <- runTBsigProfiler(indata, useAssay = "log_cpm", algorithm = "PLAGE", signatures = samp_tbsignatures, parallel.sz = 4)
```
### GSVA {.tabset}
#### Heatmap
```{r subgsva_a_TBsigs}
signatureHeatmap(gsva_res, name="GSVA", signatureColNames = names(samp_tbsignatures),
annotationColNames = c("bmi_cat2"),
showColumnNames = FALSE,
split_heatmap='none')
```
#### Boxplot
```{r boxgsva_TBsigs}
signatureBoxplot(gsva_res, name="GSVA", signatureColNames = names(samp_tbsignatures),
annotationColName = c("bmi_cat2"))# , rotateLabels = TRUE)
```
#### Boxplots Single {.tabset}
```{r , results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureBoxplot(gsva_res,
name=i,
signatureColNames = i,
annotationColName = c("bmi_cat2"),
rotateLabels = T))
cat("\n\n")
}
```
#### Signature plots {.tabset}
```{r genes_gsva_TBsigs, results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
#png(filename = paste(i, ".png"))
signatureGeneHeatmap(gsva_res, useAssay="log_cpm",
samp_tbsignatures[[i]],
name = i, signatureColNames = NULL,
annotationColNames = c("bmi_cat2",i),
showColumnNames = FALSE)
#dev.off()
cat("\n\n")
}
```
#### AUC Table {.tabset}
```{r, message = FALSE}
set.seed(0)
tableAUC(gsva_res,
annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
num.boot = 100,
pb.show = FALSE)
```
#### AUC Boxplots
```{r, message = FALSE}
set.seed(0)
compareBoxplots(gsva_res, annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
pb.show = FALSE, fill.col = "blue",
rotateLabels = TRUE)
```
#### ROC plots
```{r, message = FALSE, fig.height = 9, fig.width = 12}
signatureROCplot_CI(inputData = gsva_res,
signatureColNames = names(samp_tbsignatures),
annotationColName = "bmi_cat2")
```
#### Separate ROC plots {.tabset}
```{r, results = 'asis', message = FALSE}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureROCplot_CI(inputData = gsva_res,
signatureColNames = i,
annotationColName = "bmi_cat2",
name = paste("ROC plot,", i, sep = " ")))
cat("\n\n")
}
```
### ssGSEA {.tabset}
#### Heatmap
```{r subssgsea_a_TBsigs}
signatureHeatmap(ssgsea_res, name="ssGSEA", signatureColNames = names(samp_tbsignatures),
annotationColNames = c("bmi_cat2"),
showColumnNames = FALSE, scale = TRUE,
split_heatmap='none')
```
#### Boxplot
```{r boxssgsea_TBsigs}
signatureBoxplot(ssgsea_res, name="ssGSEA", signatureColNames = names(samp_tbsignatures),
annotationColName = c("bmi_cat2"), scale = TRUE) #rotateLabels = TRUE,
```
#### Boxplots Single {.tabset}
```{r , results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureBoxplot(ssgsea_res, name=i, signatureColNames = i,
annotationColName = c("bmi_cat2"), rotateLabels = T))
cat("\n\n")
}
```
#### Signature plots {.tabset}
```{r genes_ssgsea_TBsigs, results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
signatureGeneHeatmap(ssgsea_res, useAssay="log_cpm",
samp_tbsignatures[[i]],
name = i, signatureColNames = NULL,
annotationColNames = c("bmi_cat2",i),
showColumnNames = FALSE,
column_order = NULL)
cat("\n\n")
}
```
#### AUC Table
```{r, message = FALSE}
set.seed(0)
tableAUC(ssgsea_res,
annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
num.boot = 100,
pb.show = FALSE)
```
#### AUC Boxplots {.tabset}
```{r ssGSEA-AUC-boxplot, message = FALSE}
set.seed(0)
compareBoxplots(ssgsea_res, annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
pb.show = FALSE, fill.col = "blue",
rotateLabels = TRUE)
```
#### ROC plots
```{r, message = FALSE, fig.height = 9, fig.width = 12}
signatureROCplot_CI(inputData = ssgsea_res,
signatureColNames = names(samp_tbsignatures),
annotationColName = "bmi_cat2")
```
#### Separate ROC plots {.tabset}
```{r, results = 'asis', message = FALSE}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureROCplot_CI(inputData = ssgsea_res,
signatureColNames = i,
annotationColName = "bmi_cat2",
name = paste("ROC plot,", i, sep = " ")))
cat("\n\n")
}
```
### PLAGE {.tabset}
#### Heatmap
```{r plage_TBsigs}
signatureHeatmap(plage_res, name="PLAGE", signatureColNames = names(samp_tbsignatures),
annotationColNames = c("bmi_cat2"),
showColumnNames = FALSE,
split_heatmap='none')
```
#### Boxplot
```{r boxplage_TBsigs}
signatureBoxplot(plage_res, name="PLAGE", signatureColNames = names(samp_tbsignatures),
annotationColName = c("bmi_cat2"))# , rotateLabels = TRUE)
```
#### Boxplots Single {.tabset}
```{r , results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureBoxplot(plage_res,
name=i,
signatureColNames = i,
annotationColName = c("bmi_cat2"),
##violinPlot = TRUE,
rotateLabels = T))
cat("\n\n")
}
```
#### Signature plots {.tabset}
```{r genes_plage_TBsigs, results="asis"}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
signatureGeneHeatmap(plage_res, useAssay="log_cpm",
samp_tbsignatures[[i]],
name = i, signatureColNames = NULL,
annotationColNames = c("bmi_cat2",i),
showColumnNames = TRUE,
column_order = ordered_bmi[,1])
cat("\n\n")
}
```
#### AUC Table {.tabset}
```{r, message = FALSE}
set.seed(0)
tableAUC(plage_res,
annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
num.boot = 100,
pb.show = FALSE)
```
#### AUC Boxplots
```{r, message = FALSE}
set.seed(0)
compareBoxplots(plage_res, annotationColName = "bmi_cat2",
signatureColNames = names(samp_tbsignatures),
pb.show = FALSE, fill.col = "blue",
rotateLabels = TRUE)
```
#### ROC plots
```{r, message = FALSE, fig.height = 9, fig.width = 12}
signatureROCplot_CI(inputData = plage_res,
signatureColNames = names(samp_tbsignatures),
annotationColName = "bmi_cat2")
```
#### Separate ROC plots {.tabset}
```{r, results = 'asis', message = FALSE}
for (i in names(samp_tbsignatures)){
cat("#####", i, "\n")
print(signatureROCplot_CI(inputData = plage_res,
signatureColNames = i,
annotationColName = "bmi_cat2",
name = paste("ROC plot,", i, sep = " ")))
cat("\n\n")
}
```
### Non-normalized Enrichment Scores {.tabset}
```{r, message=FALSE, results='hide'}
# adapted runTBsigProfiler function to return non-normalized ssgsea scores
runTBsigProfiler <- function (input, useAssay = NULL, signatures = NULL, algorithm = c("GSVA",
"ssGSEA", "ASSIGN", "PLAGE", "Zscore", "singscore"), combineSigAndAlgorithm = FALSE,
assignDir = NULL, outputFormat = NULL, parallel.sz = 0, ssgsea_norm = TRUE,
ASSIGNiter = 1e+05, ASSIGNburnin = 50000)
{
if (is.null(signatures)) {
if ("TBsignatures" %in% ls(envir = .GlobalEnv)) {
get("TBsignatures", envir = .GlobalEnv)
signatures <- TBsignatures
}
else {
utils::data("TBsignatures", package = "TBSignatureProfiler",
envir = .myenv)
signatures <- .myenv$TBsignatures
}
}
runindata <- input
if (methods::is(runindata, "SummarizedExperiment")) {
if (is.null(useAssay)) {
if ("counts" %in% names(SummarizedExperiment::assays(input))) {
useAssay <- "counts"
}
else {
stop("useAssay required for SummarizedExperiment Input")
}
}
runindata <- SummarizedExperiment::assay(input, useAssay)
if (!combineSigAndAlgorithm & length(algorithm) > 1) {
stop("SummarizedExperiment not supported with combineSigAndAlgorithm FALSE.")
}
}
else if (!is.null(useAssay)) {
stop("useAssay only supported for SummarizedExperiment objects")
}
if (methods::is(runindata, "data.frame")) {
runindata <- as.matrix(runindata)
}
if (!methods::is(runindata, "matrix")) {
stop("Invalid input data type. Accepted input formats are matrix, ",
"data.frame, or SummarizedExperiment. Your input: ",
as.character(class(input)))
}
if (!all(algorithm %in% c("GSVA", "ssGSEA", "ASSIGN", "PLAGE",
"Zscore", "singscore"))) {
stop("Invalid algorithm. Supported algorithms are\n GSVA, ssGSEA, PLAGE, Zscore, singscore, and ASSIGN")
}
gsvaRes <- NULL
if ("GSVA" %in% algorithm) {
message("Running GSVA")
gsvaRes <- GSVA::gsva(runindata, signatures, parallel.sz = parallel.sz)
}
gsvaRes_ssgsea <- NULL
if ("ssGSEA" %in% algorithm) {
message("Running ssGSEA")
gsvaRes_ssgsea <- GSVA::gsva(runindata, signatures,
method = "ssgsea",
ssgsea.norm=ssgsea_norm,
parallel.sz = parallel.sz)
}
gsvaRes_PLAGE <- NULL
if ("PLAGE" %in% algorithm) {
message("Running PLAGE")
gsvaRes_PLAGE <- GSVA::gsva(runindata, signatures, method = "plage",
parallel.sz = parallel.sz)
}
gsvaRes_Z <- NULL
if ("Zscore" %in% algorithm) {
message("Running Z-score profiling")
gsvaRes_Z <- GSVA::gsva(runindata, signatures, method = "zscore",
parallel.sz = parallel.sz)
}
singscore_res <- NULL
if ("singscore" %in% algorithm) {
message("Running singscore")
singscore_res <- matrix(ncol = ncol(runindata), nrow = length(signatures),
dimnames = list(names(signatures), colnames(runindata)))
rankDat <- singscore::rankGenes(runindata)
for (sig in names(signatures)) {
singscore_res[sig, ] <- suppressWarnings(singscore::simpleScore(rankData = rankDat,
upSet = TBsignatures[[sig]], knownDirection = FALSE)$TotalScore)
}
}
assign_res <- NULL
if ("ASSIGN" %in% algorithm) {
delete_intermediate <- FALSE
if (is.null(assignDir)) {
assignDir <- tempfile("assign")
dir.create(assignDir)
delete_intermediate <- TRUE
}
else if (!dir.exists(assignDir)) {
dir.create(assignDir)
}
message("Running ASSIGN")
for (i in names(signatures)) {
message(i)
currlist <- signatures[i]
currlist[[1]] <- currlist[[1]][currlist[[1]] %in%
rownames(runindata)]
if (length(currlist[[1]]) < 2) {
message("Not enough signature genes in ", i,
",\n so analysis will not run.")
}
else {
if (!file.exists(i)) {
ASSIGN::assign.wrapper(testData = runindata,
trainingLabel = NULL, geneList = currlist,
adaptive_S = TRUE, iter = ASSIGNiter, burn_in = ASSIGNburnin,
outputDir = file.path(assignDir, i))
}
else {
message("Result already exists. Delete to re-run.")
}
}
}
assign_res <- as.matrix(t(ASSIGN::gather_assign_results(assignDir)))
if (nrow(assign_res) == 0) {
assign_res <- NULL
}
if (delete_intermediate) {
unlink(assignDir, recursive = TRUE)
}
else {
message("Intermediate ASSIGN results available at ",
assignDir)
}
}
sig_result <- NULL
if (length(algorithm) == 1) {
if (!is.null(gsvaRes)) {
sig_result <- gsvaRes
}
else if (!is.null(gsvaRes_ssgsea)) {
sig_result <- gsvaRes_ssgsea
}
else if (!is.null(assign_res)) {
sig_result <- assign_res
}
else if (!is.null(gsvaRes_Z)) {
sig_result <- gsvaRes_Z
}
else if (!is.null(gsvaRes_PLAGE)) {
sig_result <- gsvaRes_PLAGE