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Del8p12.Rmd
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Del8p12.Rmd
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---
title: "Del8p12"
author: "aluetge"
date: "2019-06-27"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Del8p12 signature
# Differentially expressed genes
### 1. Differential expression analysis
load packages
```{r packages, warning=FALSE, message=FALSE}
library(DESeq2)
library(tidyverse)
library(ggsci)
library(matrixStats)
library(piano)
library(reshape2)
library(genefilter)
library(Biobase)
library(ComplexHeatmap)
library(ggplot2)
library(gtable)
library(grid)
library(circlize)
library(gridExtra)
library(ggpubr)
library(RColorBrewer)
library(here)
```
load data
```{r datasets}
data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")
#dds data set. gene expression data + patmetadata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))
variant <- "del8p12"
#filter for patients without NA in variant
ddsCLL <- ddsCLL[, !is.na(colData(ddsCLL)[,variant])]
#differentially expressed genes between del8p12 groups (see differential expression.html)
diff_all <- read.csv(file=paste0(output_dir, "/diff_genes/", variant, "_diffGenes.csv"))
rownames(diff_all) <- diff_all$X
diff_all <- diff_all[which(diff_all$padj < 0.01 ),-1]
diff <- diff_all
mutStatus <- data.frame(colData(ddsCLL)) %>% arrange(del8p12)
colnames(ddsCLL) <-colData(ddsCLL)$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),])$symbol
```
## Expression signature
```{r complex heatmap, fig.width= 22, fig.height=25}
#colors
colors = colorRamp2(c(-4,-2,0,2,4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(del8p12 = c("1" = annocol[8], "0" = annocol[9]))
rowcolors <-colorRampPalette(brewer.pal(5, "Set1"))(5)
rowcolors[6] <- "white"
feature <- as.data.frame(colData(ddsCLL)[,c(variant)])
colnames(feature) <- c(variant)
rownames(feature) <- colnames(RNAnorm)
ha_col <- HeatmapAnnotation(df = feature, col = annocolor, annotation_height = unit(c(rep(1.9, 1)), "cm"), annotation_legend_param = list(title_gp = gpar(fontsize = 50), labels_gp = gpar(fontsize = 45), grid_height = unit(1.9, "cm"), grid_width = unit(1.9, "cm")))
h1 <- Heatmap(exprVariant.new ,
km = 2,
gap = unit(0.5, "cm"),
cluster_columns = F,
clustering_distance_rows = "pearson",
clustering_method_rows = "ward.D2",
column_title = paste0("Gene signature: ", variant),
col = colors,
column_title_gp = gpar(fontsize = 60, fontface = "bold"),
heatmap_legend_param = list(title = "expr",
title_gp = gpar(fontsize = 50),
grid_height = unit(1.9, "cm"),
grid_width = unit(1.9, "cm"),
gap = unit(2, "cm"),
labels_gp = gpar(fontsize = 45)),
column_dend_height = unit(2.5, "cm"),
show_row_dend = FALSE,
show_column_names = FALSE ,
show_row_names = TRUE,
row_names_gp = gpar(fontsize = 20),
top_annotation = ha_col)
#svg(filename=paste0(figure_dir, "/", variant, "_gene_expr.svg"), width=30, height=45)
#pdf(file=paste0(figure_dir, "/", variant, "_gene_expr.pdf"), width=22, height=25)
draw(h1 )
#dev.off()
#draw(h1)
```
# Sample and gene specific expression - top genes
```{r single gene counts, fig.width=6, fig.height=5}
#function to create stripchart plots for specific genes
gene_count <- function(gene_nam){
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% gene_nam)]
geneNum <- counts(ddsCLL)[geneEnsID,]
mutPat <- as.data.frame(colData(ddsCLL)[, c(variant)])
colnames(mutPat) <- c("genotype")
geneDat <- cbind(mutPat, geneNum)
colnames(geneDat) <- c("genotype", "counts")
p <- ggstripchart(geneDat, x = "genotype", y = "counts",
color = "genotype",
palette = "jco",
add = "mean_sd",
title = paste(gene_nam),
font.x = 18, font.y = 18, font.legend = 16,
ylab = "normalized counts") + font("xy.text", size = 15) + font("title", size = 20, face = "bold")
#ggsave(file=paste0(figure_dir, "/tri12/genetic_interaction_", gene_nam, ".svg"), plot=p, width=6, height=5)
p
}
diff <- diff_all[which(diff_all$stat > 4),]
geneList <- as.character(diff$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")
```
Run piano
```{r piano, fig.width=16, fig.height=7}
gmtFile <- gsc_Kegg
diff_res <- diff_all
diff_res$chromosome <- rowData(RNAnorm)[rownames(diff_res),]$chromosome
diff_res <- diff_res[-which(diff_res$Symbol %in% c("", NA)),]
geneNam <- diff_res$Symbol
pVal <- diff_res$padj
logFold <- diff_res$log2FoldChange
stat <- diff_res$stat
gsTab <- data.frame(gene = geneNam, stat = stat, logFold = logFold)
gsaTab <- data.frame(row.names = gsTab$gene, stat = gsTab$stat)
res <- runGSA(geneLevelStats = gsaTab,
geneSetStat = "gsea",
adjMethod = "fdr", gsc=gmtFile,
signifMethod = "geneSampling",
nPerm = 50000,
gsSizeLim=c(1, Inf))
Res_up <- arrange(GSAsummaryTable(res), `p adj (dist.dir.up)`)
Res_dn <- arrange(GSAsummaryTable(res), `p adj (dist.dir.dn)`)
#Plot
resPlot <- Res_up[, c(1:3,5,8,9)]
resPlot_dn <- Res_dn[, c(1:3,7,8,9)]
colnames(resPlot) <- c("pathway", "gene_number", "stat", "p.adj","genes_up" , "genes_dn")
colnames(resPlot_dn) <- c("pathway", "gene_number", "stat", "p.adj","genes_up" , "genes_dn")
enrichPlot <- resPlot[c(1:3),] %>% mutate(log10Padj = -log10(p.adj))
#plot
p <- ggbarplot(enrichPlot, x = "pathway", y = "log10Padj",
fill = "gene_number",
color = "white",
palette = "gsea",
sort.val = "asc",
sort.by.groups = FALSE,
ylab = "-log10(padj)",
legend.title = "#diff.genes",
rotate = TRUE,
font.x = 20, font.y = 20, font.legend = 20, legend = "right",
title = "del8p12 - Kegg",
ggtheme = theme_pubr()) +
font("xy.text", size = 16) +
font("title", size = 20, face = "bold")
#ggsave(file=paste0(figure_dir,"/GSEA_", variant, "_Kegg.svg"), plot=p, width=14, height=7)
p
```