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TP53.Rmd
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TP53.Rmd
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---
title: "TP53"
author: "almut"
date: "16 Juni 2019"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## TP53 signature
# Differentially expressed genes
### 1. Differential expression analysis
load packages
```{r packages, warning=FALSE, message=FALSE}
library(DESeq2)
library(tidyverse)
library(ggsci)
library(matrixStats)
library(piano)
library(reshape2)
library(genefilter)
library(Biobase)
library(ComplexHeatmap)
library(ggplot2)
library(gtable)
library(grid)
library(circlize)
library(gridExtra)
library(ggpubr)
library(RColorBrewer)
library(clusterProfiler)
library(msigdbr)
library(org.Hs.eg.db)
library(enrichplot)
library(here)
library(VennDiagram)
```
load data
```{r datasets}
data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")
#dds data set. gene expression data + patmetadata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))
variant <- "TP53"
#filter for patients without NA in variant
ddsCLL <- ddsCLL[, !is.na(colData(ddsCLL)[,variant])]
#differentially expressed genes between TP53 groups (see differential expression.html)
diff_all <- read.csv(file=paste0(output_dir, "/diff_genes/", variant, "_diffGenes.csv"))
rownames(diff_all) <- diff_all$X
diff_all <- diff_all[which(diff_all$padj < 0.01 ),-1]
diff <- diff_all
mutStatus <- data.frame(colData(ddsCLL)) %>% arrange(TP53)
colnames(ddsCLL) <-colData(ddsCLL)$PatID
ddsCLL <- ddsCLL[, mutStatus$PatID]
#expression data
ddsCLL <- estimateSizeFactors(ddsCLL)
RNAnorm <- varianceStabilizingTransformation(ddsCLL, blind = T)
```
Expression matrix
```{r var gene expression}
#filter for sign. genes in variant
exprMat <- assay(RNAnorm)
exprVariant <- exprMat[rownames(diff),]
colnames(exprVariant) <- colData(ddsCLL)$PatID
exprVariant.new <- log2(exprVariant)
exprVariant.new <- t(scale(t(exprVariant.new)))
exprVariant.new[exprVariant.new > 4] <- 4
exprVariant.new[exprVariant.new < -4] <- -4
rownames(exprVariant.new) <- rowData(RNAnorm[rownames(diff),])$symbol
```
## Expression signature
```{r complex heatmap, fig.width= 15, fig.height=18}
#colors
colors = colorRamp2(c(-4,-2,0,2,4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(TP53 = c("1" = annocol[8], "0" = annocol[9]))
rowcolors <-colorRampPalette(brewer.pal(5, "Set1"))(5)
rowcolors[6] <- "white"
feature <- as.data.frame(colData(ddsCLL)[,c(variant)])
colnames(feature) <- c(variant)
ha_col <- HeatmapAnnotation(df = feature, col = annocolor, annotation_height = unit(c(rep(1.9, 1)), "cm"),
simple_anno_size = unit(1, "cm"),
annotation_name_gp = gpar(fontsize = 22, fontface = "bold"),
annotation_legend_param = list(title_gp = gpar(fontsize = 23),
labels_gp = gpar(fontsize = 18),
grid_height = unit(1.2, "cm"),
grid_width = unit(1.2, "cm")))
#Annotate top 50 genes
diff <- diff_all[which(abs(diff_all$stat) > 6),]
sub_names <- unique(diff$Symbol)
geneIDs <- which(rownames(exprVariant.new) %in% sub_names)
rownames(exprVariant.new)[-geneIDs] <- ""
h1 <- Heatmap(exprVariant.new ,
km = 2,
gap = unit(0.5, "cm"),
cluster_columns = F,
clustering_distance_rows = "pearson",
clustering_method_rows = "ward.D2",
column_title = paste0("Gene signature: ", variant),
col = colors,
column_title_gp = gpar(fontsize = 25, fontface = "bold"),
heatmap_legend_param = list(title = "expr",
title_gp = gpar(fontsize = 23),
grid_height = unit(1.5, "cm"),
grid_width = unit(1.2, "cm"),
gap = unit(2, "cm"),
labels_gp = gpar(fontsize = 18)),
column_dend_height = unit(1, "cm"),
show_row_dend = FALSE,
show_column_names = FALSE ,
show_row_names = TRUE,
row_names_gp = gpar(fontsize = 17),
top_annotation = ha_col)
#svg(filename=paste0(figure_dir, "/", variant, "_gene_expr.svg"), width=30, height=45)
#pdf(file=paste0(figure_dir, "/", variant, "_gene_expr.pdf"), width=22, height=25)
draw(h1 )
#dev.off()
saveRDS(h1, file = paste0(output_dir, "/figures/r_objects/TP53/TP53_heatmap.rds"))
```
# Sample and gene specific expression - top genes
```{r single gene counts, fig.width=6, fig.height=5}
gene_count <- function(gene_nam){
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% gene_nam)]
gc <- plotCounts(ddsCLL, gene = geneEnsID, intgroup = variant, returnData=TRUE)
p <- ggboxplot(gc, x = variant, y = "count",
color = variant,
size = 1.2,
palette = "jco",
outlier.shape = NA,
add = "jitter",
add.params = list(size = 2.5),
yscale = "log10",
title = paste(gene_nam),
font.x = 20, font.y = 20, font.legend = 20,
ylab = "normalized counts") + font("xy.text", size = 20) + font("title", size = 20, face = "bold")
saveRDS(p, file = paste0(output_dir, "/figures/r_objects/TP53/de_genes/", gene_nam, ".rds"))
p
}
diff <- diff_all[which(abs(diff_all$stat) > 4.5),]
geneList <- as.character(diff$Symbol)
geneList <- geneList[-which(geneList %in% "")]
lapply(geneList, gene_count)
```
## Gene set enrichment analysis
Gene sets
```{r gene sets}
#load gene set collection
#Hallmark
gsc <- loadGSC("/home/almut/Dokumente/masterarbeit/data/h.all.v6.0.symbols.gmt", type="gmt")
#Kegg
gsc_Kegg <- loadGSC("/home/almut/Dokumente/masterarbeit/data/c2.cp.kegg.v6.0.symbols.gmt", type="gmt")
#get all de outputs
load(paste0(output_dir,"/desRes_250720.RData"))
difftab <- function(condition){
dataTab <- data.frame(res_list[[condition]])
dataTab$ID <- rownames(dataTab)
#filter using pvalues
dataTab <- dataTab %>%
arrange(padj) %>%
mutate(Symbol = rowData(ddsCLL[ID,])$symbol)# %>%
#filter(abs(log2FoldChange) > 2)
dataTab <- dataTab[!duplicated(dataTab$Symbol),]
dataTab <- dataTab[!is.na(dataTab$Symbol),]
rownames(dataTab) <- dataTab$ID
dataTab
}
diff_res <- difftab(variant)
#clusterProfiler
diff_res <- diff_res[-which(diff_res$Symbol %in% c("", NA)),]
gene_list <- diff_res$stat %>% set_names(diff_res$Symbol)
gene_list <- sort(gene_list, decreasing = TRUE)
gene_lfc <- diff_res$log2FoldChange %>% set_names(diff_res$Symbol)
gene_lfc <- sort(gene_lfc, decreasing = TRUE)
de_gene <- diff_res %>% filter(padj < 0.01)
de_gene <- de_gene$Symbol
de_ens <- diff_res %>% filter(padj < 0.01)
de_ens <- de_ens$ID
#Get Gene IDs
gene_id <- bitr(de_ens, fromType = "ENSEMBL",
toType = c("ENTREZID", "SYMBOL"),
OrgDb = org.Hs.eg.db)
gene_list_id <- bitr(diff_res$ID, fromType = "ENSEMBL",
toType = c("ENTREZID", "SYMBOL"),
OrgDb = org.Hs.eg.db)
names(gene_list_id) <- c("ID", "ENTREZID", "Symbol")
diff_id <- left_join(gene_list_id, diff_res)
gene_list_id <- diff_id$stat %>% set_names(diff_id$ENTREZID)
gene_list_id <- sort(gene_list_id, decreasing = TRUE)
gene_lfc_id <- diff_id$log2FoldChange %>% set_names(diff_id$ENTREZID)
gene_lfc_id <- sort(gene_lfc_id, decreasing = TRUE)
#convert gsc
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, human_gene_symbol)
#Hallmark
em2 <- GSEA(gene_list, TERM2GENE = m_t2g, pvalueCutoff = 0.1)
em <- enricher(de_gene, TERM2GENE = m_t2g)
#Kegg
kk <- enrichKEGG(gene_id$ENTREZID,
organism = 'hsa',
pvalueCutoff = 0.2)
kk2 <- gseKEGG(geneList = gene_list_id,
organism = 'hsa',
nPerm = 1000,
minGSSize = 50,
pvalueCutoff = 0.2,
verbose = FALSE)
kk2x <- setReadable(kk2, 'org.Hs.eg.db', 'ENTREZID')
```
Visualize ClusterProfiler results
```{r clusterProfiler}
barplot(kk, showCategory=5)
barplot(em, showCategory=5)
dot1 <- clusterProfiler::dotplot(em2, showCategory=10) + ggtitle("GSEA for TP53") +
theme_pubr() +
theme(legend.position="right") +
theme(plot.title = element_text(face = "bold"))
dot1
dotplot(em, showCategory=10) + ggtitle("Enrichment for TP53")
dotplot(kk2, showCategory=10) + ggtitle("GSEA for TP53")
dot2 <- clusterProfiler::dotplot(kk, showCategory=10) + ggtitle("Enrichment for TP53") +
theme_pubr() +
theme(legend.position="right") +
theme(plot.title = element_text(face = "bold"))
dot2
ridgeplot(em2)
ridgeplot(kk2)
gseaplot2(em2, geneSetID = 3, title = em2$Description[3])
gseaplot2(kk2, geneSetID = 2, title = kk2$Description[2])
saveRDS(dot1, file = paste0(output_dir, "/figures/r_objects/TP53/enrich_dot_hm.rds"))
saveRDS(dot2, file = paste0(output_dir, "/figures/r_objects/TP53/enrich_dot2.rds"))
```
network plot
```{r network, fig.width=10, fig.height=10}
# Networks Hallmark
em2_sub <- em2
em2_sub@result <- em2@result[which(em2@result$Description %in% c("HALLMARK_OXIDATIVE_PHOSPHORYLATION",
"HALLMARK_DNA_REPAIR",
"HALLMARK_G2M_CHECKPOINT")),]
p_net <- cnetplot(em2_sub, categorySize="pvalue", foldChange=gene_lfc) +
scale_colour_gradientn(colors = c("#581845", "#900C3F", "#C70039", "#FF5733", "#FFC300", "#DAF7A6")) +
guides(size = FALSE) +
labs(color = "logFC")
p_net
# Networks KEGG
kk2_sub <- kk2x
kk2_sub@result <- kk2x@result[which(kk2x@result$Description %in% c("p53 signaling pathway",
"Oxidative phosphorylation",
"Transcriptional misregulation in cancer"
)),]
pnet_kegg <- cnetplot(kk2_sub, categorySize="pvalue", foldChange=gene_lfc) +
scale_color_gradient(high="blue", low="red") +
guides(size = FALSE) +
labs(color = "logFC")
pnet_kegg
saveRDS(pnet_kegg, file = paste0(output_dir, "/figures/r_objects/TP53/enrich_net_kegg.rds"))
saveRDS(p_net, file = paste0(output_dir, "/figures/r_objects/TP53/enrich_net_hm.rds"))
```
heatplot
```{r heatplot, fig.width=20, fig.height=5}
heatplot(em2, foldChange=gene_lfc, showCategory = 3)
heatplot(kk2x, foldChange=gene_lfc, showCategory = 3 )
```
## Overlap with del17
```{r overlap}
#differentially expressed genes between del17p13 groups (see differential expression.html)
del17 <- read.csv(file=paste0(output_dir, "/diff_genes/del17p13_diffGenes.csv"))
rownames(del17) <- del17$X
del17 <- del17[which(del17$padj < 0.01 ),-1]
del17<- del17[,-1]
TP53 <- diff_all
TP53 <- TP53[-which(TP53$Symbol %in% ""),]
venntab <- unique(c(as.vector(del17$Symbol), as.vector(TP53$Symbol)))
vennTab_new <- as.data.frame(venntab) %>% mutate(del17p13 = ifelse(as.character(venntab) %in% del17$Symbol, 1, 0)) %>% mutate(TP53 = ifelse(as.character(venntab) %in% TP53$Symbol, 1, 0))
TP53_only <- vennTab_new %>% filter(del17p13 == 0 & TP53 == 1) %>% dplyr::select(venntab)
del17_only <- vennTab_new %>% filter(del17p13 == 1 & TP53 == 0) %>% dplyr::select(venntab)
vennTab_new <- vennTab_new[,-1]
rownames(vennTab_new) <- venntab
del17_area <- vennTab_new %>% filter(del17p13 == 1)
TP53_area <- vennTab_new %>% filter(TP53 == 1)
Both_area <- vennTab_new %>% filter(del17p13 == 1 & TP53 == 1)
#vennList <- list("del17p13" = as.vector(del17$Symbol), "TP53" = as.vector(TP53$Symbol))
#venn <- venn.diagram(vennList, filename = NULL)
venn_pair <- draw.pairwise.venn(nrow(del17_area), nrow(TP53_area), nrow(Both_area), c("del17p13", "TP53"), fill = c("#00AFBB", "#E7B800"), cex = rep(2, 3), cat.cex = rep(2, 2), ind = TRUE)
grid.draw(venn_pair)
grid.newpage()
```