Last updated: 2021-03-22

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Knit directory: transcriptome_cll/

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html	026e8e9	aluetge	2019-11-18	Build site.
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Summary Variants

Mutations in our dataset/Generate an overview of our data set - number of mutations/sample - number of cases/variant - co-occurence/corplot

load packages

suppressPackageStartupMessages({
  library(DESeq2)
  library(dplyr)
  library(ggplot2)
  library(tidyverse)
  library(corrplot)
  library(ggpubr)
  library(reshape2)
  library(here)
})

load datasets

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()

#variants <- c( "trisomy12", "del13q14", "del8p12", "gain8q24", "del11q22.3", "del17p13", "BRAF", "NOTCH1", "SF3B1","TP53", "ATM", "MED12", "IGHV")
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
pat_new <- as.tibble(colData(ddsCLL)) %>% select(c(variants, "IGHV"))

Number of recurrecnt mutations/patient

N_mut <- patMeta %>% dplyr::select(-Patient.ID) %>% mutate_if(is.factor, as.character) %>% mutate_if(is.character, as.numeric) %>% mutate(mutations=rowSums(.,na.rm = TRUE))

N_mut$IGHV <- pat_new$IGHV
N_mut <- N_mut %>% filter(!is.na(IGHV))

p <- gghistogram(N_mut, x = "mutations",
   add = "mean", bins = 12, fill = "IGHV",
  palette = "jco")

#ggsave(file=paste0(figure_dir, "/hist_mutations.svg"), plot=p, width=4, height=3)
p <- p + theme(axis.title.x = element_text(face="bold", size=25), axis.text.x=element_text(size=20), 
               axis.title.y = element_text(face="bold", size=25), axis.text.y= element_text(size=20),
               legend.title = element_text(size=25), legend.text=element_text(size=20)) 

p

Version	Author	Date
e10c2b9	aluetge	2019-11-15

saveRDS(p, file = paste0(output_dir, "/figures/r_objects/summary_variant_patient.rds"))
mean(N_mut$mutations)

[1] 3

Variant frequency

#group by IGHV status
overview <- pat_new %>% filter(!is.na(IGHV)) %>% mutate_at(vars(-IGHV), as.character) %>% mutate_at(vars(-IGHV), as.numeric) %>% dplyr::group_by(IGHV) %>% dplyr::summarize_if(is.numeric, sum, na.rm =T) 

#Add total numbers 
Sum <- pat_new %>% dplyr::select(-IGHV) %>% mutate_if(is.factor, as.character) %>% mutate_if(is.character, as.numeric) %>% summarize_if(is.numeric, sum, na.rm =T)
order_desc <- Sum[,order(as.numeric(Sum[1,]))] %>% colnames() %>% rev()

#Add IGHV status
IGHV_status <- as_data_frame(pat_new$IGHV) %>% dplyr::summarize(M = sum(value %in% "M"), U = sum(value %in% "U"), Total = n() -1)

Warning: `as_data_frame()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.

Sum$IGHV <- "Total"
IGHV_status <- t(IGHV_status)

#Combine all
sum_tab <- rbind(overview,Sum)
sum_tab <- sum_tab[, c("IGHV",order_desc)]
sum_tab <- cbind(sum_tab, IGHV_status)



sum_melted <- melt(sum_tab)

Using IGHV as id variables

#plot
p <- ggplot(sum_melted, aes(x = variable, y = factor(IGHV, levels = c("Total", "M", "U")))) +  
      geom_tile(aes(fill = value)) + 
      geom_text(aes(label = round(value, 1)), size =8) + ylab("IGHV_status") +
      scale_fill_gradient(low = "white", high = "tomato2") + 
      theme(axis.title.x = element_text(face="bold", size=30), axis.text.x=element_text(size=25, angle = 90)) + 
      theme(axis.title.y = element_text(face="bold", size=25), axis.text.y= element_text(size=20))



#pdf(file=paste0(figure_dir, "/overview_mutations.pdf"), width=15, height=5)
p

Version	Author	Date
e10c2b9	aluetge	2019-11-15

#dev.off()
saveRDS(p, file = paste0(output_dir, "/figures/r_objects/summary_var_freq_IGHV.rds"))

Variant co-occurence

variants <- c(variants, "IGHV")[!variants %in% c("gain14q32")]
#Chisquare p.adj matrix
my_chisquare <- function(variant_in){
  print(variant_in)
  pat_new_sorted <- pat_new %>% set_rownames(ddsCLL$PatID)
  pat_new_sorted <- pat_new_sorted[patMeta$Patient.ID,]
  patMeta$IGHV_mu <- pat_new_sorted$IGHV
  patMeta <- patMeta %>% mutate("IGHV" = ifelse(IGHV_mu %in% "M", 1,0))
  patMat <- as.matrix(patMeta)
  rownames(patMat) <- patMeta$Patient.ID
  variant1 <- patMat[, c("Patient.ID",variant_in)]
  rownames(variant1) <- patMeta$Patient.ID
  variant1 <- variant1[which(!is.na(variant1[,variant_in])),]
  geneBack <- patMat[rownames(variant1), variants]

  coRes <- apply(geneBack, 2, function(x) {
  nonNA <- !is.na(x)
  res <- chisq.test(variant1[,variant_in][nonNA], x[nonNA])
  data.frame(p=res$p.value, stat = res$statistic[[1]], residu = ifelse(res$residuals[1] > 0, 1, -1))
  }) %>% do.call(rbind,.)
  
  coRes$p.adj <- p.adjust(coRes$p)
  resultDat <- as.data.frame(-log10(coRes$p.adj) * coRes$residu)
  rownames(resultDat) <- rownames(coRes)
  colnames(resultDat) <- variant_in
  resultDat[variant_in,] <- 0
  resultDat
}

resChi <-lapply(variants, my_chisquare) %>% do.call(cbind,.)

[1] "gain2p25.3"
[1] "del8p12"
[1] "gain8q24"
[1] "del11q22.3"
[1] "trisomy12"
[1] "del13q14"
[1] "del15q15.1"
[1] "del17p13"
[1] "BRAF"
[1] "KRAS"
[1] "NOTCH1"
[1] "SF3B1"
[1] "TP53"
[1] "ACTN2"
[1] "ATM"
[1] "EGR2"
[1] "KLHL6"
[1] "MED12"
[1] "MGA"
[1] "NFKBIE"
[1] "PCLO"
[1] "XPO1"
[1] "IGHV"

resChiMat <- as.matrix(resChi)
resChiMat <- resChiMat[!rowSums(resChiMat) == 0, !colSums(resChiMat) == 0]

col <- colorRampPalette(rev(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA")))(50)

#svg(file=paste0(figure_dir, "/corplot_chiquare.svg"), width=10, height=10)
corrplot(resChiMat, is.corr = FALSE, method="color", col = col,
 type = "upper", order = "hclust", number.cex = 1.2,
         addCoef.col = "black",                       # Add coefficient of correlation
         tl.col = "black", tl.srt = 90,               # Text label color and rotation
         tl.cex=1.4,
         cl.lim = c(-5, 8),
         cl.cex = 1.0,
         diag = FALSE,
         title = "-log10(p.adjust) * cor_direction")

Version	Author	Date
e10c2b9	aluetge	2019-11-15

#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] here_0.1                    reshape2_1.4.3             
 [3] ggpubr_0.2                  magrittr_1.5               
 [5] corrplot_0.84               forcats_0.4.0              
 [7] stringr_1.4.0               purrr_0.3.2                
 [9] readr_1.3.1                 tidyr_0.8.3                
[11] tibble_2.1.3                tidyverse_1.2.1            
[13] ggplot2_3.1.1               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_3.1-152           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             ggsci_2.9              rprojroot_1.3-2       
[10] tools_3.6.3            backports_1.1.4        R6_2.4.0              
[13] rpart_4.1-15           Hmisc_4.2-0            DBI_1.0.0             
[16] lazyeval_0.2.2         colorspace_1.4-1       nnet_7.3-15           
[19] withr_2.1.2            tidyselect_0.2.5       gridExtra_2.3         
[22] bit_1.1-14             compiler_3.6.3         git2r_0.25.2          
[25] cli_1.1.0              rvest_0.3.4            htmlTable_1.13.1      
[28] xml2_1.2.0             labeling_0.3           scales_1.0.0          
[31] checkmate_1.9.3        genefilter_1.66.0      digest_0.6.19         
[34] foreign_0.8-76         rmarkdown_1.13         XVector_0.24.0        
[37] base64enc_0.1-3        pkgconfig_2.0.2        htmltools_0.3.6       
[40] readxl_1.3.1           htmlwidgets_1.3        rlang_0.3.4           
[43] rstudioapi_0.10        RSQLite_2.1.1          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_1.3-2          
[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             grid_3.6.3             blob_1.1.1            
[61] crayon_1.3.4           lattice_0.20-38        haven_2.1.0           
[64] splines_3.6.3          annotate_1.62.0        hms_0.4.2             
[67] locfit_1.5-9.1         knitr_1.23             pillar_1.4.1          
[70] geneplotter_1.62.0     XML_3.98-1.20          glue_1.3.1            
[73] evaluate_0.14          latticeExtra_0.6-28    modelr_0.1.4          
[76] data.table_1.12.2      cellranger_1.1.0       gtable_0.3.0          
[79] assertthat_0.2.1       xfun_0.7               xtable_1.8-4          
[82] broom_0.5.2            survival_2.44-1.1      AnnotationDbi_1.46.0  
[85] memoise_1.1.0          workflowr_1.4.0        cluster_2.1.1

summary_variants

almut

14 November 2019

Summary Variants

Number of recurrecnt mutations/patient

Variant frequency

Variant co-occurence