BRAF
almut
17 Mai 2019
Last updated: 2019-05-17
Checks: 6 0
Knit directory: transcriptome_cll/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5de142b | aluetge | 2019-05-17 | Add more variants |
BRAF signature
Differentially expressed genes
1. Differential expression analysis
load packages
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
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 <- "BRAF"
#filter for patients without NA in variant
ddsCLL <- ddsCLL[, !is.na(colData(ddsCLL)[,variant])]
#differentially expressed genes between BRAF 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[which(abs(diff_all$log2FoldChange) > 2 & diff_all$stat > 6),]
mutStatus <- data.frame(colData(ddsCLL)) %>% arrange(BRAF)
colnames(ddsCLL) <-colData(ddsCLL)$PatID
ddsCLL <- ddsCLL[, mutStatus$PatID]
#expression data
ddsCLL <- estimateSizeFactors(ddsCLL)
RNAnorm <- varianceStabilizingTransformation(ddsCLL, blind = T)Expression matrix
#filter for sign. genes in variant
exprMat <- assay(RNAnorm)
exprVariant <- exprMat[rownames(diff),]
colnames(exprVariant) <- colData(ddsCLL)$PatID
exprVariant.new <- log2(exprVariant)
exprVariant.new <- t(scale(t(exprVariant.new)))
exprVariant.new[exprVariant.new > 4] <- 4
exprVariant.new[exprVariant.new < -4] <- -4
rownames(exprVariant.new) <- rowData(RNAnorm[rownames(diff),])$symbolExpression signature
#colors
colors = colorRamp2(c(-4,-2,0,2,4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(BRAF = 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(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 = 40),
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
#function to create stripchart plots for specific genes
gene_count <- function(gene_nam){
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$symbol %in% gene_nam)]
geneNum <- counts(ddsCLL)[geneEnsID,]
mutPat <- as.data.frame(colData(ddsCLL)[, c(variant)])
colnames(mutPat) <- c("genotype")
geneDat <- cbind(mutPat, geneNum)
colnames(geneDat) <- c("genotype", "counts")
p <- ggstripchart(geneDat, x = "genotype", y = "counts",
color = "genotype",
palette = "jco",
add = "mean_sd",
title = paste(gene_nam),
font.x = 18, font.y = 18, font.legend = 16,
ylab = "normalized counts") + font("xy.text", size = 15) + font("title", size = 20, face = "bold")
#ggsave(file=paste0(figure_dir, "/tri12/genetic_interaction_", gene_nam, ".svg"), plot=p, width=6, height=5)
p
}
geneList <- diff$Symbol[1:20]
geneList <- geneList[-which(geneList %in% "")]
lapply(geneList, gene_count)[[1]]
[[2]]
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Gene set enrichment analysis
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
gmtFile <- gsc_Kegg
diff_res <- diff_all
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))Running gene set analysis:Checking arguments...done!*** Please note that running the GSEA-method may take a substantial amount of time! ***Final gene/gene-set association: 48 genes and 60 gene sets Details: Calculating gene set statistics from 48 out of 225 gene-level statistics Using all 225 gene-level statistics for significance estimation Removed 5218 genes from GSC due to lack of matching gene statistics Removed 126 gene sets containing no genes after gene removal Removed additionally 0 gene sets not matching the size limits Loaded additional information for 60 gene setsCalculating gene set statistics...done!
Calculating gene set significance...done!
Adjusting for multiple testing...done!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_dn[c(1:6),] %>% 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 = "BRAF - 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
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 here_0.1
[3] RColorBrewer_1.1-2 ggpubr_0.2
[5] magrittr_1.5 gridExtra_2.3
[7] circlize_0.4.5 gtable_0.2.0
[9] ComplexHeatmap_1.20.0 genefilter_1.64.0
[11] reshape2_1.4.3 piano_1.22.0
[13] ggsci_2.9 forcats_0.3.0
[15] stringr_1.3.1 dplyr_0.7.8
[17] purrr_0.2.5 readr_1.3.1
[19] tidyr_0.8.2 tibble_2.0.0
[21] ggplot2_3.1.0 tidyverse_1.2.1
[23] DESeq2_1.22.2 SummarizedExperiment_1.12.0
[25] DelayedArray_0.8.0 BiocParallel_1.16.5
[27] matrixStats_0.54.0 Biobase_2.42.0
[29] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
[31] IRanges_2.16.0 S4Vectors_0.20.1
[33] BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] fgsea_1.8.0 colorspace_1.3-2 rjson_0.2.20
[4] rprojroot_1.3-2 htmlTable_1.13.1 XVector_0.22.0
[7] GlobalOptions_0.1.0 base64enc_0.1-3 fs_1.2.6
[10] rstudioapi_0.9.0 bit64_0.9-7 AnnotationDbi_1.44.0
[13] lubridate_1.7.4 xml2_1.2.0 splines_3.5.3
[16] geneplotter_1.60.0 knitr_1.21 Formula_1.2-3
[19] jsonlite_1.6 workflowr_1.3.0 broom_0.5.1
[22] annotate_1.60.0 cluster_2.0.7-1 compiler_3.5.3
[25] httr_1.4.0 backports_1.1.3 assertthat_0.2.0
[28] Matrix_1.2-17 lazyeval_0.2.1 limma_3.38.3
[31] cli_1.0.1 acepack_1.4.1 htmltools_0.3.6
[34] tools_3.5.3 igraph_1.2.2 glue_1.3.0
[37] GenomeInfoDbData_1.2.0 fastmatch_1.1-0 Rcpp_1.0.0
[40] slam_0.1-44 cellranger_1.1.0 gdata_2.18.0
[43] nlme_3.1-137 xfun_0.4 rvest_0.3.2
[46] gtools_3.8.1 XML_3.98-1.16 zlibbioc_1.28.0
[49] scales_1.0.0 relations_0.6-8 hms_0.4.2
[52] sets_1.0-18 yaml_2.2.0 memoise_1.1.0
[55] rpart_4.1-13 latticeExtra_0.6-28 stringi_1.2.4
[58] RSQLite_2.1.1 checkmate_1.9.0 caTools_1.17.1.1
[61] shape_1.4.4 rlang_0.3.1 pkgconfig_2.0.2
[64] bitops_1.0-6 evaluate_0.12 lattice_0.20-38
[67] bindr_0.1.1 labeling_0.3 htmlwidgets_1.3
[70] bit_1.1-14 tidyselect_0.2.5 plyr_1.8.4
[73] R6_2.3.0 gplots_3.0.1 generics_0.0.2
[76] Hmisc_4.1-1 DBI_1.0.0 pillar_1.3.1
[79] haven_2.0.0 whisker_0.3-2 foreign_0.8-71
[82] withr_2.1.2 survival_2.43-3 RCurl_1.95-4.11
[85] nnet_7.3-12 modelr_0.1.2 crayon_1.3.4
[88] KernSmooth_2.23-15 rmarkdown_1.11 GetoptLong_0.1.7
[91] locfit_1.5-9.1 readxl_1.2.0 data.table_1.11.8
[94] marray_1.60.0 blob_1.1.1 git2r_0.25.2
[97] digest_0.6.18 xtable_1.8-3 munsell_0.5.0