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fig3_a_b_c.Rmd
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fig3_a_b_c.Rmd
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---
title: "fig3_a_b_c.Rmd"
output: html_document
date: "2023-07-18"
---
```{r setup, include=FALSE}
suppressPackageStartupMessages(library(gplots))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(ggthemes))
suppressPackageStartupMessages(library(DESeq2))
suppressPackageStartupMessages(library(limma))
suppressPackageStartupMessages(library(edgeR))
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(grid))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(UpSetR))
suppressPackageStartupMessages(library(pheatmap))
suppressPackageStartupMessages(library(gplots))
suppressPackageStartupMessages(library(kableExtra))
suppressPackageStartupMessages(library(estimate))
suppressPackageStartupMessages(library(reshape))
suppressPackageStartupMessages(library(sva))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(ggsci))
suppressPackageStartupMessages(library(RColorBrewer))
knitr::opts_chunk$set(echo = TRUE, cache=FALSE, cache.lazy = FALSE, message=FALSE, warning = FALSE)
```
```{r load-data}
counts = read.csv("gene.counts.txt", row.names=1, check.names = F)
metadata = read.csv("metadata.txt", row.names=1, check.names = F)
metadata$TCL.subtype = factor(metadata$TCL.subtype)
metadata$source = factor(metadata$source)
table(row.names(metadata) == colnames(counts))
```
# Figure 3a (PCA)
```{r pca_after_adjusting_for_sva, fig.align='center', fig.cap="Principal Component Analysis (PCA) after correction for unwanted differences between primary and xenograft samples. Surrogate variable analysis was performed via the svaseq function in the R package sva; 16 covariates were identified and corrected for prior to PCA analysis. The PCA plot is colored by TCL subtype and the shape represents source (primary or xenograft).", eval=T}
library(limma)
library(ggplot2)
# Design matrix and filtering
design <- model.matrix(~TCL.subtype, data = metadata)
filter <- filterByExpr(counts, design = design)
filtered_counts <- counts[filter,]
y <- DGEList(counts = filtered_counts, group = metadata$TCL.subtype)
y <- calcNormFactors(y)
cpm <- cpm(y, log = FALSE)
# Surrogate variable analysis
mod1 <- model.matrix(~TCL.subtype, metadata)
mod0 <- model.matrix(~source, metadata)
svseq <- svaseq(cpm, mod1, mod0)
# Batch effect removal
counts_sva <- removeBatchEffect(log(cpm + 1), covariates = svseq$sv, design = design)
# Principal component analysis
ntop <- nrow(counts_sva)
Pvars <- rowVars(counts_sva)
select <- order(Pvars, decreasing = TRUE)[seq_len(min(ntop, length(Pvars)))]
PCA <- prcomp(t(counts_sva[select, ]), scale = FALSE)
percentVar <- round(100 * PCA$sdev^2 / sum(PCA$sdev^2), 1)
dataGG <- merge(data.frame(PC1 = PCA$x[, 1], PC2 = PCA$x[, 2], PC3 = PCA$x[, 3]), metadata, by = "row.names")
# Plotting
p1 <- qplot(PC1, PC2, data = dataGG, color = TCL.subtype, shape = source, size = I(2),
main = "Principal component analysis") +
labs(x = paste0("PC1: ", round(percentVar[1], 4), "% variance"),
y = paste0("PC2: ", round(percentVar[2], 4), "% variance")) +
theme_bw() +
theme(legend.position = "right") +
scale_color_jco()
print(p1)
```
# Figure 3b heatmap -- anova top 1K
Differential gene expression analysis w/ limma
```{r limma_with_sva, eval=T}
modSv = cbind(mod1, svseq$sv)
v <- voom(y, modSv, plot=F)
fit <- lmFit(v, modSv)
fit <- eBayes(fit)
anova <- topTable(fit, coef=2:4, p.value=0.01, n=1000, lfc=1)
```
```{r heatmap_limma_anova, fig.align='center', fig.cap="Heatmap of SVA adjusted, log2 counts per million (CPM) values for top 1000 genes differentially expressed between any of the subtypes. Differential expression analysis was performed using limma-voom's ANOVA-like F-test (FDR<0.01; logFC > 1). Expression across each gene (or row) have been scaled so that mean expression is zero and standard deviation is one. Samples with relatively high expression of a given gene are colored red and samples with relatively low expression are colored gray. Lighter shades and white represent genes with intermediate expression levels. Samples and genes have been reordered by hierarchical clustering. A dendrogram is shown for the sample clustering.", fig.height=10}
# Define breaks, markers, and metadata
breaksList <- seq(-2.5, 2.5, by = 0.1)
all_markers <- row.names(anova)
sub_metadata <- metadata
# Create expression groups and assign colors
expgroups <- data.frame(tumor = sub_metadata$TCL.subtype, source = sub_metadata$source)
rownames(expgroups) <- colnames(counts_sva[, row.names(sub_metadata)])
newCols <- colorRampPalette(pal_jco("default")(length(unique(sub_metadata$TCL.subtype))))
mycolors <- newCols(length(unique(sub_metadata$TCL.subtype)))
names(mycolors) <- unique(sub_metadata$TCL.subtype)
mycolors <- list(tumor = mycolors)
# Create heatmap
pheatmap(counts_sva[all_markers, row.names(sub_metadata)],
fontsize_row = 6,
annotation_col = expgroups[, 1, drop = FALSE],
fontsize = 6,
main = "",
show_rownames = FALSE,
show_colnames = FALSE,
cluster_cols = TRUE,
cluster_rows = TRUE,
scale = "row",
colorRampPalette(rev(brewer.pal(n = 5, name = "RdGy")))(length(breaksList)),
breaks = breaksList,
annotation_colors = mycolors,
cellwidth = 3.75,
cellheight = 0.75 / (nrow(anova) / 500))
```
# Figure 3c heatmap -- GSVA
```{r read_in_signatures}
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2817630/
AITL = toupper(c("LTA","TNFSF1","LTB","TNFSF3","TNFRSF4","OX40","CD134","TNFRSF17BCM","CD269","TNFSF8","CD30L","CD153","CSF2RB","CD131","CCL17","CCL19","CCL20","CCL21CCL22","CXCL8","IL8","CXCL5","CXCL13","CXCR5","CD79a","CD19","FCRL5","CD22","IgH","IgK","IgL","MS4A1","CD20","CR1","CD21","CD23","CD24","CD37","FCRLA","AICD","ID3","SpiB","CR1","CR2","CD21","CD23","CLU","CD200","C4orf7","C1S","C3","C4A","C7","ICOS","CTLA4","SOX8","XKR4","GPR64","PTGDS","NTN2L","PLA2G2D","ALPK2","NT5DC4","IFNαR1","IFNγR1","IFNγR2","IL16","IL3RA","IL7","CCL18","CCL26","CXCL9","CXCL10","IL8","IL23A","CCR2","CSF2RA","CSF1R","CX3CR1","CXCL12","CSF2RB","CSF3R","IL-1α","IL21","IL21R","IL23","IL27Rα","IL2Rγ","LTF","IL4","IL6","IL33","IL4R","IL6R","IL6ST","TNF","TNFRSF11B","TNFRSF1B","TNFSF10","TNFSF11","TNFRSF13C","TNFSF13B","TNFSF13","TNFSF5","CD40LG","TGFβ1","TGFβR2","IL10Rα","IL10Rβ","VSTM3VSIG1","LILRB4","LILRB3","LILRB2","LILRB1","LILRA2","LIFR","LIF","CD19","BLNK","BANK1","BLK","BTK","BTLA","CD79A","CD79B","LYN","SYK","VAV3","SHB","SHCBP1","SOS1","FCGR2B","CD32","FDC-M2","C4b","CR1","CR1L","CR2","C1QBP","C1R","C2","C3","C4BPA","C4A","C5AR1","C7","CFB","CFHR1","CFP","CTSA","CTSC","CTSD","CTSH","CTSK","CTSS","CTSZ","CD10","BCL6","AICDA","GCET1","CXCR5","CD40","POU2AF1","BOB1","POU2F2","CD28","CD3δ","CD3ϵ","CD3γ","CD4","CD8α","CD8β","LCK","FYN","PTPRC","CD45","NFATC1","NFATC2IPNFATC","CD28","CD27","PCD1","PD1","BTLA","SH2D1ASAP","CD84","LY9","CD229","SLAM3LY108","SLAM6","SLAMF7","CRACC","EDG1","EDG2","EDG3","EDG4","EPHA2","EPHA3","EPHA4","EPHB1","EPHB2","FGFR2I","PCDHAC2","PCDH11X","PCDHA10","PCDHA12","ICAM1","ICAM2","ICAM3","ICAM5","VCAM1","MCM3AP","MCM5","MCM7","NEK3","NEK6","CCNC","CCND2","CCND3","CNDBP1","CCNG2","CCNI","CCNL1","CCNL2","GSPT2","CDK5R1","CDKN1B","CDC25B","CDC2L1","CDC2L2","CDC37","CDC42","CDC42BPB","CDC42EP4","CDC42EP5","CDC42SE2","CLK1","CDK2AP2","CIZ1","CRK","ERG","ETS1","FGR","FOS","JUN","MET1","MYCMYB","MAF","PVT1","BCL2","BCL2A1","BCL2L1","FAIM3"))
PMC2817630_AITL = AITL[AITL %in% row.names(counts_sva)]
ATLL = toupper(c("TCRα","TCRβ","CD1ϵ","CTLA4","CD99","TRAC","TNFRSF25","DR3","CD27","LEF1","NFATC1","CCR4","CCL13","IL23R","MAGEA4","MAGEA9","MAGEA4B","PGDS ","AICDA","MTA3","IKZF2","RGS13","PTHLH","RGS13","CADM1","c-Myc","TIAM1","PDE8B","DOK5","ARNT2","UST","PGDSMYCN","PDE8B","CEBPA","RGS13","PTHLH","SMARCA2","CD99","CADM1","TSLC1","CCR4","RGS2","SPINK2","RORA","TCRα","TCRβ","CD1ϵ","CTLA4","CD99","TRAC","TNFRSF25","DR3","CD27","LEF1","NFATC1","CCR4","CCL13","IL23R","MAGEA4","MAGEA9","MAGEA4B","PGDS","AICDA","MTA3","IKZF2","RGS13","PTHLH","RGS13","CADM1","TSLC1","c-Myc","TIAM1","PDE8B","DOK5","ARNT2","UST","PGDSMYCN","PDE8B","CEBPA","RGS13","PTHLH","SMARCA2","CD99","CADM1","TSLC1","CCR4","RGS2","SPINK2","RORA"))
PMC2817630_ATLL = ATLL[ATLL %in% row.names(counts_sva)]
ALK_pos = toupper(c("ALK","CD30","MUC1","IL26","IL31RA","IL9","IL1R2","IL17A","IL17F","RORγ","CCNA1","AGT","PDE4DIP","UPK1B","RRAD","RARα"," NRCAM","TMEM158","CA12","SERPINB3","SERPINB4","IL20","IL22","IL2RA","IL1RAP","IL1R1","CDC27","FGF5","FOSL2","VCAN","TNFRSF12A","SOCS1","SOCS3","PRF1","GZMB","LILRA3"))
PMC2817630_ALKpos = ALK_pos[ALK_pos %in% row.names(counts_sva)]
CT_ptcl = toupper(c("PRF1","GNLY","FADD","FASLG","CD244","2B4","FCGR3B","CD16","FCGR2C","CD32","CD226","DNAM-1","CD8α","CD8β","TBX21","KIR3DL1","KIR3DL2","KIR3DL3","KIR3DS1","KIR2DL1","KIR2DL2","KIR2DL3","KIR2DL4","KIR2DL5A","KIR2DS","KIR2DS2","KIR2DS3","KIR2DS5","KLRB1","KLRC2","KLRC3","KLRC4","KLRD1","KLRF1","KLRK1","CX3CR1","CXCR3","CXCL9","CXCL10","CXCL11","CCR1","CCR5","CXCR4","CCL2","CCL5","CCL8","CCL13","CCL18","XCL1","XCL2","IFNαR1","IFNγR1","IFNγR1","IFNγR2","IL1RN","IL1R1","IL1R2","IL13Rα1","IL17Rβ","IL18","IL18RAP","IL2Rβ","IL2Rγ","IL21R","IL22","IL27Rα","IL31Rα","IL32","ILF2","ILF3","TNFSF10","TNFSF13","TNFSF14","TNFRSF12α","TNFRSF1A","TNFRSF1B","TNFSF5IP1","INDO","TGFβ1","TGFβR1","IL10","IL10Rβ","VSIG4","LILRB4","LILRB3","LILRB2","LILRB1","LILRA2","CD47","SH2DIA","LY96","LY6E","SLAM-5","SLAM-6","SLAM-7","SLAM-8"))
PMC2817630_CT_PTCL = CT_ptcl[CT_ptcl %in% row.names(counts_sva)]
PMC2817630 <- list(PMC2817630_CT_PTCL=PMC2817630_CT_PTCL, "PMC2817630_ALK+"=PMC2817630_ALKpos, PMC2817630_ATLL=PMC2817630_ATLL, PMC2817630_AITL=PMC2817630_AITL)
# from giorgio
sigs = openxlsx::read.xlsx("signatures_reformated_v2.xlsx", fill=F)
sig_list = as.list(sigs)
sig_list= lapply(sig_list, function(x) toupper(x[!is.na(x)][x[!is.na(x)] %in% row.names(counts_sva)]))
sig_list_custom <- c(sig_list, PMC2817630)
```
```{r gsva, fig.width = 12, fig.height = 5, message = FALSE, message = FALSE, eval=TRUE, warning=FALSE, cache=TRUE, echo=FALSE, comment=FALSE}
library(GSVA)
esrnaseq <- gsva(as.matrix(counts_sva), sig_list_custom, method="gsva", min.sz=10, max.sz=500, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
fit <- lmFit(esrnaseq, mod1)
fit <- eBayes(fit)
anova <- topTable(fit, coef=2:4, p.value=0.05, n=500)
```
```{r gsva_heat, fig.width = 15, fig.height = 5, message = FALSE, message = FALSE, eval=TRUE, warning=FALSE, cache=TRUE, echo=FALSE, comment=FALSE, fig.cap="Heatmap of enrichment scores per sample. Single sample gene set enrichment analysis was performed with GSVA on custom gene sets using SVA adjusted log2 counts per million (CPM). Differential enrichment analysis was performed using limma's ANOVA-like F-test (FDR<0.05) on GSVA enrichment scores. Enrichment scores across each gene set (or row) have been scaled so that mean expression is zero and standard deviation is one. Samples with relatively high enrichment in a given gene set are colored red and samples with relatively low expression are colored gray Samples and gene sets have been reordered by hierarchical clustering. A dendrogram is shown for the sample clustering."}
goi = anova$ID
newCols <- colorRampPalette(pal_jco("default")(length(unique(metadata$TCL.subtype))))
mycolors <- newCols(length(unique(metadata$TCL.subtype)))
names(mycolors) <- unique(metadata$TCL.subtype)
newPdxCols <- colorRampPalette(pal_jama()(length(unique(metadata$source))))
pdxColors <- newPdxCols(length(unique(metadata$source)))
names(pdxColors) <- unique(metadata$source)
mycolors <- list(tumor = mycolors, source=pdxColors)
expgroups <- data.frame(tumor=metadata$TCL.subtype, source=metadata$source)
rownames(expgroups) <- colnames(counts_sva[,row.names(metadata)])
pheatmap(esrnaseq[goi,], scale="row", show_rownames=T,cluster_cols = T, colorRampPalette(rev(brewer.pal(n = 5, name = "RdGy")))(length(breaksList)), breaks = breaksList, cellwidth=7, cellheight=9, fontsize_row = 10, annotation_col=expgroups, annotation_colors=mycolors, fontsize_col = 7)
```
# Session Info
```{r session, message=FALSE, warning=FALSE, cache=FALSE,echo=FALSE, fig.width=10, fig.height=5.5, context="data"}
sessionInfo()
```