ATM
aluetge
2019-06-29
Last updated: 2019-11-13
Checks: 7 0
Knit directory: transcriptome_cll/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8f9f56f | aluetge | 2019-11-13 | wflow_publish(c(“analysis/ATM.Rmd”, “analysis/Del13q14.Rmd”)) |
| html | cc24f92 | aluetge | 2019-07-28 | Build site. |
ATM 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 <- "ATM"
#filter for patients without NA in variant
ddsCLL <- ddsCLL[, !is.na(colData(ddsCLL)[,variant])]
#differentially expressed genes between ATM 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$stat) > 4.5) ,]
mutStatus <- data.frame(colData(ddsCLL)) %>% arrange(ATM)
colnames(ddsCLL) <-colData(ddsCLL)$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),])$symbolExpression signature
#colors
colors = colorRamp2(c(-4,-2,0,2,4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(ATM = c("1" = annocol[8], "0" = annocol[9]))
rowcolors <-colorRampPalette(brewer.pal(5, "Set1"))(5)
rowcolors[6] <- "white"
feature <- as.data.frame(colData(ddsCLL)[,c(variant)])
colnames(feature) <- c(variant)
rownames(feature) <- colnames(RNAnorm)
ha_col <- HeatmapAnnotation(df = feature, col = annocolor, annotation_height = unit(c(rep(1.9, 1)), "cm"), annotation_legend_param = list(title_gp = gpar(fontsize = 50), labels_gp = gpar(fontsize = 45), grid_height = unit(1.9, "cm"), grid_width = unit(1.9, "cm")))
h1 <- Heatmap(exprVariant.new ,
km = 2,
gap = unit(0.5, "cm"),
cluster_columns = F,
clustering_distance_rows = "pearson",
clustering_method_rows = "ward.D2",
column_title = paste0("Gene signature: ", variant),
col = colors,
column_title_gp = gpar(fontsize = 60, fontface = "bold"),
heatmap_legend_param = list(title = "expr",
title_gp = gpar(fontsize = 50),
grid_height = unit(1.9, "cm"),
grid_width = unit(1.9, "cm"),
gap = unit(2, "cm"),
labels_gp = gpar(fontsize = 45)),
column_dend_height = unit(2.5, "cm"),
show_row_dend = FALSE,
show_column_names = FALSE ,
show_row_names = TRUE,
row_names_gp = gpar(fontsize = 20),
top_annotation = ha_col)
#svg(filename=paste0(figure_dir, "/", variant, "_gene_expr.svg"), width=30, height=45)
#pdf(file=paste0(figure_dir, "/", variant, "_gene_expr.pdf"), width=22, height=25)
draw(h1 ) 
| Version | Author | Date |
|---|---|---|
| cc24f92 | aluetge | 2019-07-28 |
#dev.off()
#draw(h1)Sample and gene specific expression - top genes
#function to create stripchart plots for specific genes
gene_count <- function(gene_nam){
geneEnsID <- rownames(ddsCLL)[which(rowData(ddsCLL)$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
}
diff <- diff_all[which(diff_all$stat > 4),]
geneList <- as.character(diff$Symbol)
geneList <- geneList[-which(geneList %in% "")]
lapply(geneList, gene_count)[[1]]
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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$chromosome <- rowData(RNAnorm)[rownames(diff_res),]$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))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: 6 genes and 27 gene sets Details: Calculating gene set statistics from 6 out of 23 gene-level statistics Using all 23 gene-level statistics for significance estimation Removed 5260 genes from GSC due to lack of matching gene statistics Removed 159 gene sets containing no genes after gene removal Removed additionally 0 gene sets not matching the size limits Loaded additional information for 27 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(1:5),] %>% 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 = "ATM - 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
| Version | Author | Date |
|---|---|---|
| cc24f92 | aluetge | 2019-07-28 |
sessionInfo()R version 3.6.0 (2019-04-26)
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] here_0.1 RColorBrewer_1.1-2
[3] ggpubr_0.2 magrittr_1.5
[5] gridExtra_2.3 circlize_0.4.6
[7] gtable_0.3.0 ComplexHeatmap_2.0.0
[9] genefilter_1.66.0 reshape2_1.4.3
[11] piano_2.0.2 ggsci_2.9
[13] forcats_0.4.0 stringr_1.4.0
[15] dplyr_0.8.1 purrr_0.3.2
[17] readr_1.3.1 tidyr_0.8.3
[19] tibble_2.1.3 ggplot2_3.1.1
[21] tidyverse_1.2.1 DESeq2_1.24.0
[23] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[25] BiocParallel_1.18.0 matrixStats_0.54.0
[27] Biobase_2.44.0 GenomicRanges_1.36.0
[29] GenomeInfoDb_1.20.0 IRanges_2.18.1
[31] S4Vectors_0.22.0 BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 Hmisc_4.2-0
[4] fastmatch_1.1-0 workflowr_1.4.0 plyr_1.8.4
[7] igraph_1.2.4.1 lazyeval_0.2.2 shinydashboard_0.7.1
[10] splines_3.6.0 digest_0.6.19 htmltools_0.3.6
[13] gdata_2.18.0 checkmate_1.9.3 memoise_1.1.0
[16] cluster_2.1.0 limma_3.40.2 annotate_1.62.0
[19] modelr_0.1.4 colorspace_1.4-1 blob_1.1.1
[22] rvest_0.3.4 haven_2.1.0 xfun_0.7
[25] crayon_1.3.4 RCurl_1.95-4.12 jsonlite_1.6
[28] survival_2.44-1.1 glue_1.3.1 zlibbioc_1.30.0
[31] XVector_0.24.0 GetoptLong_0.1.7 shape_1.4.4
[34] scales_1.0.0 DBI_1.0.0 relations_0.6-8
[37] Rcpp_1.0.1 xtable_1.8-4 htmlTable_1.13.1
[40] clue_0.3-57 foreign_0.8-71 bit_1.1-14
[43] Formula_1.2-3 DT_0.7 htmlwidgets_1.3
[46] httr_1.4.0 fgsea_1.10.0 gplots_3.0.1.1
[49] acepack_1.4.1 pkgconfig_2.0.2 XML_3.98-1.20
[52] nnet_7.3-12 locfit_1.5-9.1 labeling_0.3
[55] tidyselect_0.2.5 rlang_0.3.4 later_0.8.0
[58] AnnotationDbi_1.46.0 munsell_0.5.0 cellranger_1.1.0
[61] tools_3.6.0 visNetwork_2.0.7 cli_1.1.0
[64] generics_0.0.2 RSQLite_2.1.1 broom_0.5.2
[67] evaluate_0.14 yaml_2.2.0 knitr_1.23
[70] bit64_0.9-7 fs_1.3.1 caTools_1.17.1.2
[73] nlme_3.1-140 whisker_0.3-2 mime_0.7
[76] slam_0.1-45 xml2_1.2.0 compiler_3.6.0
[79] rstudioapi_0.10 png_0.1-7 marray_1.62.0
[82] geneplotter_1.62.0 stringi_1.4.3 lattice_0.20-38
[85] Matrix_1.2-17 shinyjs_1.0 pillar_1.4.1
[88] GlobalOptions_0.1.0 data.table_1.12.2 bitops_1.0-6
[91] httpuv_1.5.1 R6_2.4.0 latticeExtra_0.6-28
[94] promises_1.0.1 KernSmooth_2.23-15 gtools_3.8.1
[97] assertthat_0.2.1 rprojroot_1.3-2 rjson_0.2.20
[100] withr_2.1.2 GenomeInfoDbData_1.2.1 hms_0.4.2
[103] rpart_4.1-15 rmarkdown_1.13 git2r_0.25.2
[106] sets_1.0-18 shiny_1.3.2 lubridate_1.7.4
[109] base64enc_0.1-3