Differentially expressed genes
almut
15 November 2019
Last updated: 2021-03-22
Checks: 7 0
Knit directory: transcriptome_cll/
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Differentially expressed genes in CLL
Aim: Find gene signatures for mutational status of CLL patients
load packages
library(DESeq2)
library(dplyr)
library(magrittr)
library(tidyverse)
library(gridExtra)
library(ComplexHeatmap)
library(matrixStats)
library(here)
library(reshape2)
library(DT)
library(xlsx)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"))
#load meta data including genotyping info
load(paste0(data_dir, "/patmeta_170324.RData"))
excluded_columns <- c("HIPO.ID","PID", "gender","project", "diagnosis", "date.of.diagnosis", "treatment", "date.of.first.treatment", "IGHV.status","Methylation_Cluster")
patMeta <- as.tibble(patMeta) %>% filter(Patient.ID %in% ddsCLL$PatID) %>% dplyr::select(-one_of(excluded_columns))Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.variants <- patMeta %>% dplyr::select(-Patient.ID) %>%
mutate_if(is.factor, as.character) %>%
mutate_if(is.character, as.numeric) %>%
dplyr::select(colnames(.)[colSums(.,na.rm = TRUE) > 4]) %>%
colnames()
var_add <- variants[!variants %in% names(colData(ddsCLL))]
rownames(patMeta) <- patMeta$Patient.IDWarning: Setting row names on a tibble is deprecated.patMeta <- patMeta[colData(ddsCLL)$PatID,]
cd <- cbind(colData(ddsCLL), patMeta[,var_add])
colData(ddsCLL) <- cd###Deseq
ddsCLL <- estimateSizeFactors(ddsCLL)
#write a function to perform deseq for different genetic conditions
diff <- function(cond){
gc()
ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"IGHV"])]
ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"trisomy12"])]
colData(ddsCLL_new)[,"IGHV"] <-droplevels(colData(ddsCLL_new)[,"IGHV"])
design(ddsCLL_new) <- as.formula(paste("~ IGHV + trisomy12 + ", paste(cond)))
rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
res <- results(rnaRaw)
resOrdered <- res[order(res$pvalue),]
}
#gene_conditions <- c("del13q14", "del8p12", "gain8q24", "del11q22.3", "del17p13", "BRAF", "NOTCH1", "SF3B1", "TP53", "ATM", "MED12", "trisomy12")
variants_not12 <- variants[!variants %in% "trisomy12"]
#res_list <- lapply(variants_not12, diff)
#names(res_list) <- variants_not12
diff_notri12 <- function(cond){
ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"trisomy12"])]
design(ddsCLL_new) <- as.formula(paste("~ trisomy12 + ", paste(cond)))
rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
res <- results(rnaRaw)
resOrdered <- res[order(res$pvalue),]
}
#res_list[["IGHV"]] <- diff_notri12("IGHV")
diff_noighv <- function(cond){
ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"IGHV"])]
design(ddsCLL_new) <- as.formula(paste("~ IGHV + ", paste(cond)))
rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
res <- results(rnaRaw)
resOrdered <- res[order(res$pvalue),]
}
#res_list[["trisomy12"]] <- diff_noighv("trisomy12")
#save(res_list, file=paste0(output_dir,"/desRes_250720.RData"))
load(paste0(output_dir,"/desRes_250720.RData"))Diff genes
pCut <- 0.1
difftab <- function(condition){
dataTab <- data.frame(res_list[[condition]])
dataTab$ID <- rownames(dataTab)
#filter using pvalues
dataTab <- filter(dataTab, padj <= pCut) %>%
arrange(padj) %>%
mutate(Symbol = rowData(ddsCLL[ID,])$symbol) %>%
filter(abs(log2FoldChange) > 1)
dataTab <- dataTab[!duplicated(dataTab$Symbol),]
dataTab <- dataTab[!is.na(dataTab$Symbol),]
rownames(dataTab) <- dataTab$ID
#write.csv(dataTab, file=paste0(output_dir,"/diff_genes/", condition, "_diffGenes.csv"))
dataTab
}
cond <- c(variants, "IGHV")
#Only run when you want to write result tables! Change path according to test!
sigRes <- lapply(cond, difftab)
names(sigRes) <- condMA-plots
for (condition in cond) {
cat("### ", condition, " {-}\n")
DESeq2::plotMA(res_list[[condition]], ylim=c(-5,5), main= paste(condition))
cat("\n\n")
}
SF3B1
TP53
gain14q32
ACTN2
ATM
EGR2
KLHL6
MED12
MGA
NFKBIE
PCLO
XPO1
IGHV
# myMaPlot <-function(condition){
# DESeq2::plotMA(res_list[[condition]], ylim=c(-5,5), main= paste(condition))
# }
#
# lapply(cond, myMaPlot)Histogramm of pvalues
for (condition in cond) {
cat("### ", condition, " {-}\n")
res <- res_list[[condition]]
hist(res$pvalue, breaks=100, col="skyblue", border="slateblue",
main=paste0("Histogramm of pvalues:", condition),plot = TRUE)
cat("\n\n")
}
SF3B1
TP53
gain14q32
ACTN2
ATM
EGR2
KLHL6
MED12
MGA
NFKBIE
PCLO
XPO1
IGHV
# myHist <- function(condition){
# res <- res_list[[condition]]
# hist(res$pvalue, breaks=100, col="skyblue", border="slateblue",
# main=paste0("Histogramm of pvalues:", condition),plot = TRUE)
# }
#
# hist_cond <- lapply(cond, myHist)DE genes
del8p12
dataTab <- data.frame(res_list[["del8p12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)gain8q24
dataTab <- data.frame(res_list[["gain8q24"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)del11q22.3
dataTab <- data.frame(res_list[["del11q22.3"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)trisomy12
dataTab <- data.frame(res_list[["trisomy12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)del13q14
dataTab <- data.frame(res_list[["del13q14"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)del17p13
dataTab <- data.frame(res_list[["del17p13"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)BRAF
dataTab <- data.frame(res_list[["BRAF"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)NOTCH1
dataTab <- data.frame(res_list[["NOTCH1"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)SF3B1
dataTab <- data.frame(res_list[["SF3B1"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)TP53
dataTab <- data.frame(res_list[["TP53"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)ATM
dataTab <- data.frame(res_list[["ATM"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)MED12
dataTab <- data.frame(res_list[["MED12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)IGHV
dataTab <- data.frame(res_list[["IGHV"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)save results
wb <- createWorkbook()
sheets <- lapply(cond, createSheet, wb = wb)
void <- Map(addDataFrame, sigRes, sheets)
saveWorkbook(wb, file = paste0(output_dir,"/de_genes_all.xlsx"))
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.7 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=de_DE.UTF-8
[9] LC_ADDRESS=de_DE.UTF-8 LC_TELEPHONE=de_DE.UTF-8
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=de_DE.UTF-8
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] xlsx_0.6.5 DT_0.17
[3] reshape2_1.4.3 here_0.1
[5] ComplexHeatmap_2.0.0 gridExtra_2.3
[7] forcats_0.4.0 stringr_1.4.0
[9] purrr_0.3.2 readr_1.3.1
[11] tidyr_0.8.3 tibble_2.1.3
[13] ggplot2_3.1.1 tidyverse_1.2.1
[15] magrittr_1.5 dplyr_0.8.1
[17] DESeq2_1.24.0 SummarizedExperiment_1.14.0
[19] DelayedArray_0.10.0 BiocParallel_1.18.0
[21] matrixStats_0.54.0 Biobase_2.44.0
[23] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[25] IRanges_2.18.1 S4Vectors_0.22.0
[27] BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 rjson_0.2.20 rprojroot_1.3-2
[4] circlize_0.4.6 htmlTable_1.13.1 XVector_0.24.0
[7] GlobalOptions_0.1.0 base64enc_0.1-3 fs_1.3.1
[10] clue_0.3-57 rstudioapi_0.10 bit64_0.9-7
[13] AnnotationDbi_1.46.0 lubridate_1.7.4 xml2_1.2.0
[16] splines_3.6.3 geneplotter_1.62.0 knitr_1.23
[19] Formula_1.2-3 jsonlite_1.6 workflowr_1.4.0
[22] rJava_0.9-13 broom_0.5.2 annotate_1.62.0
[25] cluster_2.1.1 png_0.1-7 shiny_1.3.2
[28] compiler_3.6.3 httr_1.4.0 backports_1.1.4
[31] assertthat_0.2.1 Matrix_1.3-2 lazyeval_0.2.2
[34] cli_1.1.0 later_0.8.0 acepack_1.4.1
[37] htmltools_0.3.6 tools_3.6.3 gtable_0.3.0
[40] glue_1.3.1 GenomeInfoDbData_1.2.1 Rcpp_1.0.1
[43] cellranger_1.1.0 nlme_3.1-152 crosstalk_1.0.0
[46] xfun_0.7 xlsxjars_0.6.1 rvest_0.3.4
[49] mime_0.7 XML_3.98-1.20 zlibbioc_1.30.0
[52] scales_1.0.0 promises_1.0.1 hms_0.4.2
[55] RColorBrewer_1.1-2 yaml_2.2.0 memoise_1.1.0
[58] rpart_4.1-15 latticeExtra_0.6-28 stringi_1.4.3
[61] RSQLite_2.1.1 genefilter_1.66.0 checkmate_1.9.3
[64] shape_1.4.4 rlang_0.3.4 pkgconfig_2.0.2
[67] bitops_1.0-6 evaluate_0.14 lattice_0.20-38
[70] htmlwidgets_1.3 bit_1.1-14 tidyselect_0.2.5
[73] plyr_1.8.4 R6_2.4.0 generics_0.0.2
[76] Hmisc_4.2-0 DBI_1.0.0 pillar_1.4.1
[79] haven_2.1.0 whisker_0.3-2 foreign_0.8-76
[82] withr_2.1.2 survival_2.44-1.1 RCurl_1.95-4.12
[85] nnet_7.3-15 modelr_0.1.4 crayon_1.3.4
[88] rmarkdown_1.13 GetoptLong_0.1.7 locfit_1.5-9.1
[91] readxl_1.3.1 data.table_1.12.2 blob_1.1.1
[94] git2r_0.25.2 digest_0.6.19 xtable_1.8-4
[97] httpuv_1.5.1 munsell_0.5.0