Last updated: 2021-03-22

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Knit directory: transcriptome_cll/

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Summary DE genes by variants

Aim: Generate a summary plot giving an overview on the number of differntially expressed genes / number upreagulated / number downregulated genes and chromosomal distribution for CNVs.

libraries

suppressPackageStartupMessages({
  library(tidyverse)
  library(ggplot2)
  library(here)
  library(ggpubr)
  library(ggsci)
  library(RColorBrewer)
  library(circlize)
  library(reshape2)
  library(ggsci)
  library(DESeq2)
})

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) %>% dplyr::select(-Chromothripsis) %>% 
  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

#variants <- c( "trisomy12", "del13q14", "del8p12", "gain8q24", "del11q22.3", "del17p13", "BRAF", "NOTCH1", "SF3B1","TP53", "ATM", "MED12","IGHV")

variants <- c(variants, "IGHV")

Summarize de genes in a table

#Function to summarize de genes
variant_stats <- function(variant, chr){
   diff_genes <-read.csv(file=paste0(output_dir, "/diff_genes/", variant, "_diffGenes.csv"))
  sig_genes <- as.tibble(diff_genes) %>% filter(padj < 0.01 & abs(log2FoldChange) > 0) 
  up_genes <- sig_genes %>% filter(log2FoldChange > 0) %>% mutate(chrom = rowData(ddsCLL)$chromosome[which(rownames(ddsCLL) %in% X)])
  dn_genes <- sig_genes %>% filter(log2FoldChange < 0) %>% mutate(chrom = rowData(ddsCLL)$chromosome[which(rownames(ddsCLL) %in% X)])
  chr_up <- ifelse(!is.na(chr), length(which(up_genes$chrom %in% chr)), 0)
  chr_dn <- ifelse(!is.na(chr), length(which(dn_genes$chrom %in% chr)), 0)
  
  summary <- tibble(
    variant = variant,
    n_genes = nrow(sig_genes),
    up_genes = nrow(up_genes),
    dn_genes = nrow(dn_genes),
    nr_up = chr_up,
    nr_dn = chr_dn,
    nr_chrom = chr_dn + chr_up,
    nr_other = n_genes - chr_dn - chr_up
  )
  }

# Variant chromosomal location
chromo <- c( "2", "8", "8", "11", "12", "13", "15", "17", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)
input <- cbind(variants, chromo)
input

      variants     chromo
 [1,] "gain2p25.3" "2"   
 [2,] "del8p12"    "8"   
 [3,] "gain8q24"   "8"   
 [4,] "del11q22.3" "11"  
 [5,] "trisomy12"  "12"  
 [6,] "del13q14"   "13"  
 [7,] "del15q15.1" "15"  
 [8,] "del17p13"   "17"  
 [9,] "BRAF"       NA    
[10,] "KRAS"       NA    
[11,] "NOTCH1"     NA    
[12,] "SF3B1"      NA    
[13,] "TP53"       NA    
[14,] "gain14q32"  NA    
[15,] "ACTN2"      NA    
[16,] "ATM"        NA    
[17,] "EGR2"       NA    
[18,] "KLHL6"      NA    
[19,] "MED12"      NA    
[20,] "MGA"        NA    
[21,] "NFKBIE"     NA    
[22,] "PCLO"       NA    
[23,] "XPO1"       NA    
[24,] "IGHV"       NA

summary <- mapply(variant_stats, variants, chromo)
sum_dat <- data.frame(matrix(unlist(summary), nrow=length(variants), byrow=T),stringsAsFactors=FALSE)
colnames(sum_dat) <- c("variant", "n_genes", "up_genes", "dn_genes", "nr_up", "nr_dn", "nr_chrom", "nr_other")
sum_dat <- sum_dat %>% filter(as.numeric(n_genes) >= 100)
sum_order <- as.tibble(sum_dat) %>% arrange(desc(-1*as.numeric(n_genes))) %>% dplyr::select(variant)

#sum_dat_long <- melt(sum_dat[,c("variant","up_genes", "dn_genes")], id.vars = c("variant")) #, variable_name = c("up_genes", "dn_genes"))

sum_dat_long <- melt(sum_dat[,c("variant","nr_other", "nr_chrom")], id.vars = c("variant")) #, variable_name = c("up_genes", "dn_genes"))

colnames(sum_dat_long) <- c("variant", "location", "genes")
sum_dat_long$dir_color <-paste0(sum_dat_long$variant, "_", sum_dat_long$location)

sum_dat_long$genes <- as.numeric(as.character(sum_dat_long$genes))
sum_dat_long <- as.tibble(sum_dat_long) %>% arrange(desc(genes)) %>% mutate(group=ifelse(variant %in% c("trisomy12", "IGHV"), 1, 2))

Plots

Summary all variants

col_pal1 <- pal_igv(palette = c("default"), alpha = 1)(nrow(sum_dat))
col_pal2 <- pal_igv(palette = c("default"), alpha = 0.4)(nrow(sum_dat))

color <- c(col_pal1, col_pal2)
color <- sort(color)

#pdf(file=paste0(figure_dir, "/sum_diffGenes_0.05_2.pdf"), width=8, height=5)
ggbarplot(sum_dat_long[order(match(sum_dat_long$variant, sum_order$variant)),], "variant", "genes",
  fill = "dir_color", palette = color,
  label = FALSE,  font.x =23 ,font.y = 23, font.tickslab = 18, x.text.angle = 90, legend = "none")

Version	Author	Date
026e8e9	aluetge	2019-11-18
f9c1040	aluetge	2019-11-14

#dev.off()

IGHV and trisomy12

sum_dat_long1 <- as.tibble(sum_dat_long) %>% filter(variant %in% c("trisomy12", "IGHV"))

#pdf(file=paste0(figure_dir, "/sum_diffGenes_noTsig_IGHVTri12.pdf"), width=5, height=2.3)
p1 <- ggbarplot(sum_dat_long1[order(match(sum_dat_long1$variant, sum_order$variant)),], "variant", "genes",
  fill = "dir_color", palette = color[c(1:4)],
  label = FALSE,  font.x =23 ,font.y = 23, font.tickslab = 18, x.text.angle = 0, legend = "none", rotate = T) 
#dev.off()

p1

Version	Author	Date
026e8e9	aluetge	2019-11-18
f9c1040	aluetge	2019-11-14

saveRDS(p1, file = paste0(output_dir, "/figures/r_objects/summary_de_genes_IGHV_tri12.rds"))

All other

sum_dat_long2 <- as.tibble(sum_dat_long) %>% filter(!variant %in% c("trisomy12", "IGHV")) %>% filter

#pdf(file=paste0(figure_dir, "/sum_diffGenes_noTsig.pdf"), width=6, height=7.5)
p2 <- ggbarplot(sum_dat_long2[order(match(sum_dat_long2$variant, sum_order$variant)),], "variant", "genes",
  fill = "dir_color", palette = color[c(5:50)],
  label = FALSE,  font.x =23 ,font.y = 23, font.tickslab = 18, x.text.angle = 0, legend = "none", rotate = T) 
#dev.off()


p2

Version	Author	Date
026e8e9	aluetge	2019-11-18
f9c1040	aluetge	2019-11-14

saveRDS(p2, file = paste0(output_dir, "/figures/r_objects/summary_de_genes_all.rds"))

summary table

#pdf(file=paste0(output_dir,"/sum_diffGenes_table.pdf"), width=5, height=7.5)
sum_dat

     variant n_genes up_genes dn_genes nr_up nr_dn nr_chrom nr_other
1 del11q22.3     137       58       79     1    42       43       94
2  trisomy12    3679     1806     1873   520    14      534     3145
3   del13q14     257       49      208     1     9       10      247
4   del17p13     277      171      106    14    66       80      197
5       BRAF     227      193       34     0     0        0      227
6      SF3B1     455      306      149     0     0        0      455
7       TP53     157      100       57     0     0        0      157
8       IGHV    3022     1769     1253     0     0        0     3022

#dev.off()

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=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] DESeq2_1.24.0               SummarizedExperiment_1.14.0
 [3] DelayedArray_0.10.0         BiocParallel_1.18.0        
 [5] matrixStats_0.54.0          Biobase_2.44.0             
 [7] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
 [9] IRanges_2.18.1              S4Vectors_0.22.0           
[11] BiocGenerics_0.30.0         reshape2_1.4.3             
[13] circlize_0.4.6              RColorBrewer_1.1-2         
[15] ggsci_2.9                   ggpubr_0.2                 
[17] magrittr_1.5                here_0.1                   
[19] forcats_0.4.0               stringr_1.4.0              
[21] dplyr_0.8.1                 purrr_0.3.2                
[23] readr_1.3.1                 tidyr_0.8.3                
[25] tibble_2.1.3                ggplot2_3.1.1              
[27] tidyverse_1.2.1            

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1       rprojroot_1.3-2        htmlTable_1.13.1      
 [4] XVector_0.24.0         GlobalOptions_0.1.0    base64enc_0.1-3       
 [7] fs_1.3.1               rstudioapi_0.10        bit64_0.9-7           
[10] AnnotationDbi_1.46.0   lubridate_1.7.4        xml2_1.2.0            
[13] splines_3.6.3          geneplotter_1.62.0     knitr_1.23            
[16] Formula_1.2-3          jsonlite_1.6           workflowr_1.4.0       
[19] broom_0.5.2            annotate_1.62.0        cluster_2.1.1         
[22] compiler_3.6.3         httr_1.4.0             backports_1.1.4       
[25] assertthat_0.2.1       Matrix_1.3-2           lazyeval_0.2.2        
[28] cli_1.1.0              acepack_1.4.1          htmltools_0.3.6       
[31] tools_3.6.3            gtable_0.3.0           glue_1.3.1            
[34] GenomeInfoDbData_1.2.1 Rcpp_1.0.1             cellranger_1.1.0      
[37] nlme_3.1-152           xfun_0.7               rvest_0.3.4           
[40] XML_3.98-1.20          zlibbioc_1.30.0        scales_1.0.0          
[43] hms_0.4.2              yaml_2.2.0             memoise_1.1.0         
[46] gridExtra_2.3          rpart_4.1-15           latticeExtra_0.6-28   
[49] stringi_1.4.3          RSQLite_2.1.1          genefilter_1.66.0     
[52] checkmate_1.9.3        shape_1.4.4            rlang_0.3.4           
[55] pkgconfig_2.0.2        bitops_1.0-6           evaluate_0.14         
[58] lattice_0.20-38        labeling_0.3           htmlwidgets_1.3       
[61] bit_1.1-14             tidyselect_0.2.5       plyr_1.8.4            
[64] R6_2.4.0               generics_0.0.2         Hmisc_4.2-0           
[67] DBI_1.0.0              pillar_1.4.1           haven_2.1.0           
[70] whisker_0.3-2          foreign_0.8-76         withr_2.1.2           
[73] survival_2.44-1.1      RCurl_1.95-4.12        nnet_7.3-15           
[76] modelr_0.1.4           crayon_1.3.4           rmarkdown_1.13        
[79] locfit_1.5-9.1         grid_3.6.3             readxl_1.3.1          
[82] data.table_1.12.2      blob_1.1.1             git2r_0.25.2          
[85] digest_0.6.19          xtable_1.8-4           munsell_0.5.0

Summary DE genes

almut

14 November 2019