Last updated: 2020-09-13

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Knit directory: transcriptome_cll/

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Unstaged changes:
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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)

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.ID

Warning: 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) > 2)
  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) <- cond

MA-plots

#Check Ma plots

myMaPlot <-function(condition){
  DESeq2::plotMA(res_list[[condition]], ylim=c(-5,5), main= paste(condition))
}

lapply(cond, myMaPlot)

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Histogramm of pvalues

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)

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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] reshape2_1.4.3              here_0.1                   
 [3] ComplexHeatmap_2.0.0        gridExtra_2.3              
 [5] forcats_0.4.0               stringr_1.4.0              
 [7] purrr_0.3.2                 readr_1.3.1                
 [9] tidyr_0.8.3                 tibble_2.1.3               
[11] ggplot2_3.1.1               tidyverse_1.2.1            
[13] magrittr_1.5                dplyr_0.8.1                
[15] DESeq2_1.24.0               SummarizedExperiment_1.14.0
[17] DelayedArray_0.10.0         BiocParallel_1.18.0        
[19] matrixStats_0.54.0          Biobase_2.44.0             
[21] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
[23] IRanges_2.18.1              S4Vectors_0.22.0           
[25] BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] nlme_3.1-140           bitops_1.0-6           fs_1.3.1              
 [4] lubridate_1.7.4        bit64_0.9-7            RColorBrewer_1.1-2    
 [7] httr_1.4.0             rprojroot_1.3-2        tools_3.6.0           
[10] backports_1.1.4        R6_2.4.0               rpart_4.1-15          
[13] Hmisc_4.2-0            DBI_1.0.0              lazyeval_0.2.2        
[16] colorspace_1.4-1       GetoptLong_0.1.7       nnet_7.3-12           
[19] withr_2.1.2            tidyselect_0.2.5       bit_1.1-14            
[22] compiler_3.6.0         git2r_0.25.2           cli_1.1.0             
[25] rvest_0.3.4            htmlTable_1.13.1       xml2_1.2.0            
[28] scales_1.0.0           checkmate_1.9.3        genefilter_1.66.0     
[31] digest_0.6.19          foreign_0.8-71         rmarkdown_1.13        
[34] XVector_0.24.0         base64enc_0.1-3        pkgconfig_2.0.2       
[37] htmltools_0.3.6        GlobalOptions_0.1.0    readxl_1.3.1          
[40] htmlwidgets_1.3        rlang_0.3.4            rstudioapi_0.10       
[43] RSQLite_2.1.1          shape_1.4.4            generics_0.0.2        
[46] jsonlite_1.6           acepack_1.4.1          RCurl_1.95-4.12       
[49] GenomeInfoDbData_1.2.1 Formula_1.2-3          Matrix_1.2-17         
[52] Rcpp_1.0.1             munsell_0.5.0          stringi_1.4.3         
[55] whisker_0.3-2          yaml_2.2.0             zlibbioc_1.30.0       
[58] plyr_1.8.4             blob_1.1.1             crayon_1.3.4          
[61] lattice_0.20-38        haven_2.1.0            splines_3.6.0         
[64] annotate_1.62.0        circlize_0.4.6         hms_0.4.2             
[67] locfit_1.5-9.1         knitr_1.23             pillar_1.4.1          
[70] rjson_0.2.20           geneplotter_1.62.0     XML_3.98-1.20         
[73] glue_1.3.1             evaluate_0.14          latticeExtra_0.6-28   
[76] modelr_0.1.4           data.table_1.12.2      png_0.1-7             
[79] cellranger_1.1.0       gtable_0.3.0           clue_0.3-57           
[82] assertthat_0.2.1       xfun_0.7               xtable_1.8-4          
[85] broom_0.5.2            survival_2.44-1.1      AnnotationDbi_1.46.0  
[88] memoise_1.1.0          workflowr_1.4.0        cluster_2.1.0

Differentially expressed genes

almut

15 November 2019

Differentially expressed genes in CLL

MA-plots

Histogramm of pvalues