GSEA of Proteomics

Last updated: 2021-02-19

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Knit directory: 2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/

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Introduction

Obtain Proteomics differential analysis outcome: YAP shRNA D28 Proteomics results_ES.txt

Used libraries and functions

java/1.8.0_66
parallel

library(ggplot2)
library(grid)
library(gridExtra)
library(cowplot)


********************************************************

Note: As of version 1.0.0, cowplot does not change the

  default ggplot2 theme anymore. To recover the previous

  behavior, execute:
  theme_set(theme_cowplot())

********************************************************

library(ggpubr)


Attaching package: 'ggpubr'

The following object is masked from 'package:cowplot':

    get_legend

library(dplyr)


Attaching package: 'dplyr'

The following object is masked from 'package:gridExtra':

    combine

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Read files

name = read.delim("/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190531_RNA_run1/seqaln/rename/mrna_fulllen_pe_strrev.mx.chr", header = T, sep = "\t")
name$Gene.names = gsub(".*_","",name$Geneid)
name = name[,c(1,7)]

prote = read.delim("/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/1.1kbpTSS/8.intersectwithproteomic/YAP shRNA D28 Proteomics results_ES.txt", header = T, sep = "\t")

mg = merge(prote, name, by = "Gene.names")
mg = mg[,c(21,2:20)]

write.table(mg, file = "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/Github/2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/output/YAP_shRNA_D28_Proteomics_results.xls", col.names = T, row.names = F, sep = "\t")

Rank Genes and change mouse gene name to human homolog

Following generated edgeR spreadsheet, use the logFC and p.Value to generate a rank score using the following scripts.

rnkgenM2H.sh

  #!/bin/bash

  #Specify the input file
  XLS=$1
  #Specify the gene ID column
  ID=$2
  #Specify the fold change value column
  FC=$3
  #Specify the raw p-value column
  P=$4
  #Specify ortholog maping
  ORTH=$5


  RNK=${XLS}.rnk
  HUM=${RNK}.hum.rnk

  sed 1d $XLS | tr -d '"' \
  | awk -v I=$ID -v F=$FC -v P=$P '{FS="\t"} {print $I, $F, $P}' \
  | awk '$2!="NA" && $3!="NA"' \
  | awk '{s=1} $2<0{s=-1} {print $1"\t"s*-1*log($3)/log(10)}' \
  | sort -k2gr | sed 's/inf$/330/'> $RNK

  sed 's/_/\t/' $RNK \
  | sort -k 1b,1 \
  | join -1 2 -2 1 $ORTH - \
  | awk '{OFS="\t"} {print $0,$5*$5}' \
  | sort -k6gr \
  | awk '!arr[$4]++' \
  | awk '{OFS="\t"} !arr[$3]++ {print $3,$5}' \
  | sort -k2gr > $HUM

Run rnkgenM2H.sh to generate .rnk files

  #!/bin/bash
  for XLS in *xls ; do
     ./rnkgen.sh $XLS 1 5 6 mouse2human.txt.sort 1 2 3 ;
  done

Run GSEA using ranked genes

Subject the generated .rnk files along with .gmt file sand run the following scripts to perform gene set enrichment analysis.

Download gmt files from GSEA webpage http://www.gsea-msigdb.org/gsea/login.jsp;jsessionid=C4D3892651A8792A331D7B32E9D2269C

rungsea.sh

  #!/bin/bash

  run_gsea(){
  RNK=$1
  GMT=$2
  echo /group/card2/Evangelyn_Sim/NGS/app/gsea-3.0.jar $RNK $GMT
  java -Xmx4096m -cp /group/card2/Evangelyn_Sim/NGS/app/gsea-3.0.jar xtools.gsea.GseaPreranked  \
  -gmx $GMT -collapse false -mode Max_probe \
  -norm meandiv -nperm 1000 -rnk $RNK -scoring_scheme classic \
  -rpt_label ${RNK}.${GMT} -include_only_symbols true -make_sets true \
  -plot_top_x 20 -rnd_seed timestamp -set_max 5000 -set_min 10 -zip_report false \
  -out . -gui false
  }
  export -f run_gsea

  parallel -j5 run_gsea ::: *rnk ::: *gmt

Parse GSEA outcome

#!/bin/bash
echo 'GeneSetName   GeneSetSize ES  NES p-val   FDR FWER' > header.txt

for GSEADIR in `ls | grep GseaPreranked | grep -v xls$` ; do
  awk '{FS="\t"} {OFS="\t"} $8<0.05 {print $1,$4,$5,$6,$7,$8,$9} ' $GSEADIR/gsea_report_for_na_*xls \
  | cat header.txt - > $GSEADIR.xls
done

Plot GSEA result

files = list.files(path = "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/1.1kbpTSS/10.proteomic_gsea", pattern = ".*go.xls$", full.names = T)
mx = lapply(files, read.delim, header=T)


for(l in 1:length(mx)){
  
  mx[[l]]$GeneSetName = gsub("GO_", "", mx[[l]]$GeneSetName)
  
  mxRU= mx[[l]]
  mxRU= mxRU[order(mxRU$NES, decreasing = T), ]
  mxRU= mxRU[c(1:5),]
  mxRU$colour = "blue"
  
  mxRD= mx[[l]]
  mxRD= mxRD[order(mxRD$NES), ]
  mxRD= mxRD[c(1:5),]
  mxRD$colour = "red"
  
  ES_all = rbind(mxRU, mxRD)
  
}

par(las =2)
par(mar=c(3,55,5,2))
  
fig = barplot(rev(ES_all$NES), 
                horiz = T, 
                col = ES_all$colour, 
                names.arg = rev(ES_all$GeneSetName) , 
                cex.axis = 1.5, cex.names = 1.25)

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] dplyr_1.0.2     ggpubr_0.4.0    cowplot_1.0.0   gridExtra_2.3  
[5] ggplot2_3.3.2   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.18         purrr_0.3.4       haven_2.3.1      
 [5] carData_3.0-4     colorspace_1.4-1  vctrs_0.3.2       generics_0.1.0   
 [9] htmltools_0.5.0   yaml_2.2.1        rlang_0.4.7       later_1.1.0.1    
[13] pillar_1.4.6      foreign_0.8-71    glue_1.4.2        withr_2.3.0      
[17] readxl_1.3.1      lifecycle_0.2.0   stringr_1.4.0     cellranger_1.1.0 
[21] munsell_0.5.0     ggsignif_0.6.0    gtable_0.3.0      zip_2.1.1        
[25] evaluate_0.14     knitr_1.30        rio_0.5.16        forcats_0.5.0    
[29] httpuv_1.5.4      curl_4.3          highr_0.8         broom_0.7.0      
[33] Rcpp_1.0.5        promises_1.1.1    scales_1.1.1      backports_1.1.10 
[37] abind_1.4-5       fs_1.5.0          hms_0.5.3         digest_0.6.27    
[41] openxlsx_4.2.3    stringi_1.5.3     rstatix_0.6.0     rprojroot_1.3-2  
[45] tools_3.6.1       magrittr_1.5      tibble_3.0.3      crayon_1.3.4     
[49] whisker_0.4       tidyr_1.1.2       car_3.0-10        pkgconfig_2.0.3  
[53] ellipsis_0.3.1    data.table_1.13.2 rmarkdown_2.5     rstudioapi_0.11  
[57] R6_2.5.0          git2r_0.27.1      compiler_3.6.1