ComputeProbsFromLogisticRegression.R

library(parallel)
library("poibin")

args<-commandArgs(TRUE)
probFile<-args[1] #"~/Documents/Lab/SnyderLab/LocalMutationRate/WindowProbs/RegionProbs_chr22_1.csv"
countsFile<-args[2] #"/Users/cmelton/Documents/Aptana Studio 3 Workspace/GenomeScanner/output/Mar5/OneBP/RawOutput/WholeGenomeWindows_1a.1b.1d.1f.2a.2b.2c.3a.3b.4.5_chr22_1.txt"
outputFile<-args[3]
size<-as.numeric(args[4])
print(args[4])
print(size)

# read in counts and probabilities for a chromosome
probs<-read.csv(probFile, header=T, sep=",", stringsAsFactors=F)
probs$regionID<-sapply(probs$regionID, function(prob) paste(strsplit(x=prob, split="_", fixed=T)[[1]][1],as.character(as.numeric(strsplit(x=prob, split="_", fixed=T)[[1]][2])+size-1), sep="_"))
#get rid of redundant cols
realNames<-sapply(colnames(probs), function(prob) strsplit(x=prob, split=".[x,y]", fixed=F)[[1]])
print(realNames)
probs<-probs[,!duplicated(realNames)]
colnames(probs)<-sapply(colnames(probs), function(prob) strsplit(x=prob, split=".[x,y]", fixed=F)[[1]])
pids<-colnames(probs)[2:length(probs)]
pids[sapply(pids, nchar)==37]<-substr(pids[sapply(pids, nchar)==37], 2, 37)
colnames(probs)[2:length(probs)]<-pids
counts<-read.delim(countsFile, header=T, stringsAsFactors=F)
counts$regionID<-paste(counts$chrom, counts$pos, sep="_")

# TestAndTrainWithPID<-merge(TestAndTrain, coverageData[!duplicated(coverageData$idAnalysisInfo), c("idAnalysisInfo", "participant_id")], by="idAnalysisInfo", all.x=T)
pidSubsets<-list(test=c('9938ce5c-e74e-446e-a932-f096f85cc3b1','b58ad350-5140-4fa8-bc2c-24bca8395f3a','8cf54607-01ce-42b0-9bd9-8627edd9f3b7','328dfcb0-b5ae-43f2-bc87-dad0ab3df9e7','b5fba77b-1f50-4e71-95b2-566afba4bdd7','18eb4dfc-556f-4bf3-a411-4780209ed1e0','08da7c4c-3067-4bcf-9d7a-78566df72e69','084c6aec-94f7-4090-8f3f-59fa9e89721a','7861a5f2-8910-4bcb-9c34-f79c5acd6e21','5580b21a-2cdb-4777-ad79-6e06654144f5','784de7ac-8424-42eb-83d4-a1bebaa42b97','1557183c-aedd-4df6-b98a-3654667f7b69','a9b7d7fe-be31-4f71-afee-c1bfdf511888','1174f6e4-ffbe-4e59-a000-8d861c968369','566792ae-f853-4a47-856d-f02cdcfcb18a','786e8dbe-442e-4551-87b3-b4c333b04dd4','d67cd793-2931-429a-9084-2f3c4c8be7ad','5a57dc25-d252-4b22-b192-4de630d7002f','f6e1ec78-5ad9-4879-9b7e-262d17b166ad','c0892598-1f7b-4f23-9cd8-731f797753d5','324bcba2-f6a4-45a6-807c-215bdffcca21','8bd68696-5eeb-46d7-9dc6-c39c849029a4','ccd4a24b-d8cc-4686-9dee-c98b0c5a8d21','27b05b15-a44b-45ed-a6e3-e7d1ca488ea9','045c13ef-3db7-4adf-b0a3-23338f0479f3','2c86c3ea-d926-4d39-a5ae-39ece4774287','7d3e77cc-9603-4722-8e39-1912b678871b','3edf933a-be1a-46e8-bfb0-acdff30e64a0','b913d254-8307-4b8a-8313-d978e32bb38f','ce8612ab-3149-4a6a-b424-29c0c21c9b8b','1f11de87-a5fe-49fd-80c1-e279b2bc69de','a02932f3-5606-42a5-aee4-e5b9f5aa1aaa','220be009-8f1e-4a77-ab04-3220b5f87ea6','585e6487-b0a3-4828-8a06-46bee01dff74','6fd72426-f6c8-47ca-a500-d5d3600b9b15','fd9ee494-65fe-4de4-adff-7952a059b17f','43abe847-4ba7-466e-8283-5d7b80b999a7','0e2ee54a-51c9-4868-842d-a2a1c1cfb016','0ab8d063-62b4-4d47-82aa-e3351a60029d','8aaa4e25-5c12-4ace-96dc-91aaa0c4457c','1e417b93-837d-4110-9090-239eb82c3e20','3f93d1cf-a307-4e29-9e6f-239e9ffaa179','cc353686-e2b6-46a1-8095-48e2bc44b11f','4edff57f-4b0e-4770-beac-590da7d7232c','5338d435-68fb-4f0d-a3e6-c843f703f75f','9f632fc3-2241-469a-a14a-cd537350155f','2ed3296a-5b10-40d8-8af4-90dc031657cd','cfc999aa-802e-4a0d-8b14-be2b19fa2a17','28e9b6f9-ab6a-400b-a6a9-79095a443591','9f5c0a43-d222-484a-9249-d288387dd7a1','900b5f21-e8fb-4a3f-b8e3-ef098716e508',
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                         'f86fa219-34e9-4812-8edf-2b66d7219779','0eda7373-8335-455f-b90a-8c3540f28088','cce62116-3dcd-400d-ada0-9839ca02466e','9aabcbd7-a26a-49b1-abf1-02c0dc3adcf2','419deaac-ea45-4bdd-9fa0-b5cd8429b44f','5017c115-39b8-4c5c-8be6-2cb298d7c641','f8c0a69c-df53-4a0a-a30e-4424c0ce3dd5','7941d1c7-5319-4e8d-a15f-60b61cb68a9b','1cc53ade-73c0-492b-a7dc-328269fd0e66','3ed614e7-f356-4d87-985b-d3bbbae3bb40','0c50a2c2-1d4c-45ca-9127-f96d4db18daf','02ec7a3a-2812-4a65-93cd-07bf655cc91b','61eb08d0-ea01-45e7-884b-ac3d6070172a','904a8757-e0c5-41ef-b583-c8f170caaac1','f435ec00-0db8-46b4-96bf-890a9d931df3','010aac75-3bfe-4bf2-b866-af0f2d92f125','8d3e3d09-bc61-4d5e-b8d6-5aee6cb488ae','56a82a56-0241-4d3d-9de2-696b0c36df91','178b28cd-99c3-48dc-8d09-1ef71b4cee80','84569c1a-6baa-4380-ae3c-707df1be4618','a435fdf4-a89a-499e-9006-cbb5693eab26','9c274536-3ca1-4f0e-93a8-1688074d862f','18c08546-bed3-4ed9-b223-479afe633c8e','e733f289-a0ec-478c-aad9-1378ad26ddfa','01c4ca12-ac08-41dd-9e4f-bfb0971688a2','cede5274-279d-4021-8aca-5ecc4bf94d66','8fccff71-3710-4cb9-b8af-61707aebe5d8','072507cf-1249-4b28-86d4-3ab97822a74c','2a971e32-b0e7-4a3f-8c40-62d69c0307d8','b0cb6ed7-3d9f-4d5e-8df5-edcc921c43b4','e4b08e66-0772-4956-8670-e5c6553eecbe','f9cf605f-f287-4eba-8e8f-8ba47e14ecc4','941f75a1-fea4-4539-ba69-60bb11608f6d','fef9c64f-5959-4da0-aaa2-66b56fc7b4c3','00ad0ffe-2105-4829-a495-1c2aceb5bb31','db1e3104-8bfd-4353-854a-c478ba1f7d07'))

# [TestAndTrainWithPID$test]
# &TestAndTrainWithPID$short_disease_type%in%c('BRCA','LUSC','KIRC','GBM','HNSC','UCEC','LUAD','OV')
# paste(TestAndTrainWithPID$participant_id[!TestAndTrainWithPID$short_disease_type%in%c('BRCA','LUSC','KIRC','GBM','HNSC','UCEC','LUAD','OV')], collapse="','")
# paste(TestAndTrain$idAnalysisInfo[!TestAndTrain$short_disease_type%in%c('BRCA','LUSC','KIRC','GBM','HNSC','UCEC','LUAD','OV')], collapse="','")
# paste(TestAndTrain$idAnalysisInfo, collapse="','")
# paste(unique(TestAndTrain$short_disease_type), collapse="','")
# counts and probs merged
m<-merge(probs, counts[,c("regionID", "number_of_tumors", "number_of_tumors_test", "number_of_tumors_train",
                          "number_of_tumors_all", "number_of_tumors_testEight", "number_of_tumors_trainEight",
                          "number_of_tumors_allEight", "number_of_tumors_other")], by="regionID", all.x=T, all.y=F)
m<-m[!is.na(m$number_of_tumors),]

# function to compute poisson binomial prob k>=K for a row of merged data
getProb<-function(data, i, pids, countcol="number_of_tumors") {
  pids<-gsub(pattern = "-", replacement = ".", x = pids)
  pids<-pids[pids%in%colnames(data)]
  print(length(pids))
  probs<-as.numeric(data[i,pids])
  cts<-as.numeric(data[i,countcol])
  print(cts)
  return(1-ppoibin(cts-1, probs))
}

#print(dim(m))
# compute probabilities

probs<-sapply(1:length(m$regionID), function(i) getProb(m, i, pids))

# generate results data frame
result<-data.frame(regionID=m$regionID, prob=probs, number_of_tumors=m$number_of_tumors)
result$test<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["test"]], countcol="number_of_tumors_test"))
result$train<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["train"]], countcol="number_of_tumors_train"))
result$all<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["all"]], countcol="number_of_tumors_all"))
result$testEight<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["testEight"]], countcol="number_of_tumors_testEight"))
result$trainEight<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["trainEight"]], countcol="number_of_tumors_trainEight"))
result$allEight<-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["allEight"]], countcol="number_of_tumors_allEight"))
result$other <-sapply(1:length(m$regionID), function(i) getProb(m, i, pidSubsets[["other"]], countcol="number_of_tumors_other"))

# write output to file
write.csv(result, file=outputFile, row.names=F, quote=F)