R/analysis/DrugSPIA.R

In asifemon/msdrp: Pathway Signatures 4 Drug Repurposing

#' 3rd script
#' summary:
#' This script calculates pathway enrichment analysis using Signaling Pathway Impact (SPIA) with:
#' 01: genes that are drug perturbed, differentially expressed and having SNPs from GWASs and/or
#' 02: genes that are drug perturbed and having SNPs from GWASs

suppressWarnings(suppressMessages(library(data.table)))
suppressWarnings(suppressMessages(library(dplyr)))
suppressWarnings(suppressMessages(library(tidyr)))

suppressWarnings(suppressMessages(library(foreach)))
suppressWarnings(suppressMessages(library(doParallel)))
registerDoParallel(parallel::detectCores() - 2)

suppressWarnings(suppressMessages(library(SPIA)))

#####################################################################
#TODO: Change to the directory where you cloned this repository
#~~~~~~~Using relative path~~~~~~~#
ensureFolder = function(folder) {
  if (! file.exists(folder)) {
    dir.create(folder)
  }
}

args = commandArgs(trailingOnly = TRUE)
resultsFolder = normalizePath(args[1])
ensureFolder(resultsFolder)
sprintf("Using results folder at %s", resultsFolder)

dataFolder = file.path(resultsFolder)
#####################################################################


#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#~~~~~~~~~~~~~~~~~~~: SPIA Functions :~~~~~~~~~~~~~~~~~~~#

spia_fun = function(x){
  spia_kegg_drug <- foreach(i = seq(lfc_test)) %dopar% {
    foreach(j = seq(lfc_test[[i]])) %dopar% {
      drug = lfc_test[[i]][[j]]
      tmp=spia(de = drug, all = entrez_all, data.dir = file.path(dataFolder,"spia_input/real_kegg/"), organism = "hsa")
    }
  }
}

name_fun = function(spia_kegg_drug){
  for (disease in 1:length(spia_kegg_drug)) {
    names(spia_kegg_drug)[[disease]] = names(lfc_test)[[disease]]
    for (drug in 1:length(spia_kegg_drug[[disease]])) {
      names(spia_kegg_drug[[disease]])[[drug]] = names(lfc_test[[disease]])[[drug]]
    }
  }
  spia_kegg_drug
}

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#~~~~~~~~~~~: Drug Data Preperation for SPIA :~~~~~~~~~~~#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

load(file.path(dataFolder,"geneID_v97.RData"))
gene_id$ENTREZ = gsub("^$", NA, gene_id$ENTREZ)
gene_id = gene_id[which(! is.na(gene_id$ENTREZ)),]
gene_id = gene_id[, c(1, 2)]

# get LINCS dataset
load(file.path(dataFolder,"L1000.RData"))
L1000 = L1000[, c(5, 1, 6)]

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#' User can use any Drug response dataset here with 3 columns: 
#' "chembl.id", "ensembl.id", "fold change" 
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

L1000 = L1000[order(ensembl.id, decreasing = TRUE),]
L1000 = L1000[! duplicated(L1000[, c('ensembl.id', 'chembl.id')]),]
L1000 = merge(L1000, gene_id, by = "ensembl.id")

load(file.path(dataFolder,"drug2715details.RData"))
dmap = data.table(dmap[, c(1, 2, 3)])
dmap = dmap[phase == 4 | phase == 3 | phase == 2 | phase == 1] #673 Drugs
L1000 = merge(dmap[, c(1, 2)], L1000, by = "chembl.id")
# length(unique(L1000$chembl.id))

#' Users can select any of following two data sets 

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#~~~~~~~~~~~01: Drug Perturbed GWAS Genes :~~~~~~~~~~~~~~#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# load(file.path(dataFolder,"results/drugGWAS.genes50.padj1e-5.RData"))
# super_drugs = drugGWAS.genes[,c(2,3,4,9)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#~~~~02: Drug Perturbed Disease Genes (DEGs_GWAS) :~~~~~~#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
load(file.path(dataFolder,"results/drugPdisease_genes.padj.RData"))
super_drugs = drugPdisease_genes[, c(2, 3, 4, 9)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#' to select only those diseases from disease_genes50
load(file.path(dataFolder,"results/disease_genes.RData"))
disease_genes = disease_genes[p.adjusted <= 0.05]
disease_genes = unique(disease_genes[, c(1, 2)])
super_drugs = merge(super_drugs, disease_genes, by.x = "efo.id", by.y = "efo.id.DEGs")
length(unique(super_drugs$efo.id))

# split multiple genes in same column to multiple rows
drug_genes = unique(super_drugs %>%
    mutate(ensembl.id = strsplit(as.character(commonGenes), ",")) %>%
    unnest(ensembl.id))
#clean up special symbols
drug_genes$ensembl.id = gsub("\"", "", drug_genes$ensembl.id)
drug_genes$ensembl.id = gsub("c\\(", "", drug_genes$ensembl.id)
drug_genes$ensembl.id = gsub("\\)", "", drug_genes$ensembl.id)
drug_genes$ensembl.id = trimws(drug_genes$ensembl.id)

sprintf("Number of unique ChEMBL terms: %d", length(unique(drug_genes$chembl.name)))
sprintf("Number of unique EFO terms: %d", length(unique(drug_genes$efo.id)))
sprintf("Number of unique Genes: %d", length(unique(drug_genes$ensembl.id)))

#' Since we compare the Drug SPIA results with Disease SPIA
#' we will filter out all the entries from drug_genes which 
#' do not match the EFO.ID from Disease SPIA
#' 
load(file.path(dataFolder,"spia_input/lfc_disease_genes.RData"))
Diseases = as.data.frame(names(lfc_efo))
names(Diseases) = "efo.term"
drug_genes = merge(Diseases, drug_genes, by.x = "efo.term", by.y = "efo.term.DEGs")

#' now merge with clinically trialled Drugs only from L1000 Data
drug_genes = merge(drug_genes, L1000, by = c('chembl.name', 'ensembl.id')) # merge with L1000
drug_genes = drug_genes[! duplicated(drug_genes[, c('chembl.id', 'ensembl.id', 'efo.term')]),]

#' count efo_chembl pair frequency, to remove rows frequencies less than 10
# tmp = count(drug_genes, c('efo.term','chembl.id'))
# tmp2 = subset(tmp, freq> 10)
# drug_genes = merge(tmp2,drug_genes, by="efo.term") # memory problem

topDiseases = split(drug_genes, drug_genes$efo.term) # split on diseases first
topDiseases_drug = lapply(topDiseases, function(x) split(x, x$chembl.name)) # split on drug inside each disease
topDiseases_drug = topDiseases_drug[lapply(topDiseases_drug, length) >= 10] # remove diseases with very few drugs to test
# topDiseases_drug = topDiseases_drug[lapply(topDiseases_drug, length) > 0]


### remove chemble.name, chemble.id and efo.term columns sicne they are not required any more
for (element in 1 : length(topDiseases_drug)) {
    for (subelem in 1 : length(topDiseases_drug[[element]])) {
        topDiseases_drug[[element]][[subelem]]$chembl.name = NULL
        topDiseases_drug[[element]][[subelem]]$chembl.id = NULL
        topDiseases_drug[[element]][[subelem]]$efo.id = NULL
        topDiseases_drug[[element]][[subelem]]$efo.term = NULL
        topDiseases_drug[[element]][[subelem]]$efo.term.y = NULL
    }
}

save(topDiseases_drug, file = file.path(dataFolder,"spia_input/lfc_drug_genes.RData"))

####____Create named entity list for each drug_disease pairs___####

# load(file.path(dataFolder,"spia_input/drug_genes.list.drugPdiseaseGenes50.padj.RData"))

lfc_drug_ensembl = topDiseases_drug
for (i in 1 : length(topDiseases_drug)) {
    for (j in 1 : length(topDiseases_drug[[i]])) {
        lfc_drug_ensembl[[i]][[j]] = setNames(as.numeric(topDiseases_drug[[i]][[j]][[2]]), as.character(topDiseases_drug[[i]][[j]][[1]]))
    }
}

lfc_drug_entrez = topDiseases_drug
for (i in 1 : length(topDiseases_drug)) {
    for (j in 1 : length(topDiseases_drug[[i]])) {
        lfc_drug_entrez[[i]][[j]] = setNames(as.numeric(topDiseases_drug[[i]][[j]][[2]]), as.character(topDiseases_drug[[i]][[j]][[3]]))
    }
}

lfc_drug_entrezID = topDiseases_drug
for (i in 1 : length(topDiseases_drug)) {
    for (j in 1 : length(topDiseases_drug[[i]])) {
        lfc_drug_entrezID[[i]][[j]] = setNames(as.numeric(topDiseases_drug[[i]][[j]][[2]]), as.character(gsub("^", "ENTREZID:", topDiseases_drug[[i]][[j]][[3]])))
    }
}

##_____Create a vector with all Gene universe to proxy Array Genes_________###
load(file.path(dataFolder,"geneID_v97.RData"))
ensembl_all = unique(gene_id$ensembl.id)
entrez_all = unique(gene_id$ENTREZ)
entrezID_all = unique(gsub("^", "ENTREZID:", gene_id$ENTREZ)) ## with ENTREZID: in front of each id

save(lfc_drug_ensembl, ensembl_all, file = file.path(dataFolder,"spia_input/lfc_drugPdisease_ensembl_namedVector.RData"))
save(lfc_drug_entrez, entrez_all, file = file.path(dataFolder,"spia_input/lfc_drugPdisease_entrez_namedVector.RData"))
save(lfc_drug_entrezID, entrezID_all, file = file.path(dataFolder,"spia_input/lfc_drugPdisease_entrezID_namedVector.RData"))


#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#~~~~~~~~~~~~~~~~~~~~~~: Drug SPIA :~~~~~~~~~~~~~~~~~~~~~#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#~~~~~~~~~~~~~~~~~~~: Drug SPIA - KEGG :~~~~~~~~~~~~~~~~~#

load(file.path(dataFolder,"spia_input/lfc_drugPdisease_entrez_namedVector.RData"))
lfc_test = lfc_drug_entrez

spia_kegg_drug = spia_fun(lfc_test)
spia_kegg_drug = name_fun(spia_kegg_drug)

save(spia_kegg_drug, file = file.path(dataFolder,"results/spia_output/spia_kegg_drug.RData"))

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#~~~~~~~~~~~~~~~~~: Drug SPIA - Reactome :~~~~~~~~~~~~~~~#

load(file.path(dataFolder,"spia_input/lfc_drugPdisease_entrezID_namedVector.RData"))
lfc_test = lfc_drug_entrezID

spia_reactome_drug = spia_fun(lfc_test)
spia_reactome_drug = name_fun(spia_reactome_drug)

save(spia_reactome_drug, file = file.path(dataFolder,"results/spia_output/spia_reactome_drug.RData"))

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#~~~~~~~~~~~~~~~~~: Drug SPIA - Biocarta :~~~~~~~~~~~~~~~#

load(file.path(dataFolder,"spia_input/lfc_drugPdisease_entrezID_namedVector.RData"))
lfc_test = lfc_drug_entrezID

spia_biocarta_drug = spia_fun(lfc_test)
spia_biocarta_drug = name_fun(spia_biocarta_drug)

save(spia_biocarta_drug, file = file.path(dataFolder,"results/spia_output/spia_biocarta_drug.RData"))

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#