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Copy pathbetareg_phil_unadjusted_rm_NA_afro.R
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betareg_phil_unadjusted_rm_NA_afro.R
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#setwd("/media/PEP_USB/LRRI2013/Phil/betareg/data/ADC_SNP_data")
#ADC.meth.filenames <- list.files("/media/PEP_USB/LRRI2013/Phil/betareg/data/meth_genes/ADC_meth_genes/", full.names=TRUE)
#ADC.SNP.filenames <- list.files("/media/PEP_USB/LRRI2013/Phil/betareg/data/ADC_SNP_data", full.names=TRUE)
#myGG <- qplot(x=reg.table[,5], y=reg.table[,1], xlab="SNP_A.1790605", ylab="cg27542552", main="BVs vs. SNP values for cg27542552 and SNP_A.1790605 ")
setwd("D:/Phil/betareg/data/")
library(betareg)
meth_filename_split <- function(string){
x <- strsplit(string, split = "_")[[1]][8]
y <- strsplit(x, split = "[.]")[[1]][1]
return(y)
}
SNP_filename_split <- function(string){
x <- strsplit(string, split = "_")[[1]][4]
y <- strsplit(x, split = "/")[[1]][2]
return(y)
}
SNP_list_parse <- function(string){
x <- strsplit(string, split = "-")[[1]]
y <- paste(x[1],x[2], sep=".")
return(y)
}
calc_allele_freq <- function(SNP){
x <- length(which(SNP == 0)) #AA
y <- length(which(SNP == 1)) #Aa
z <- length(which(SNP == 2)) #aa
a.freq <- (2*z + y)/(2*(x + y + z))
return(a.freq)
}
#contains probe_ID, probe_name, Chromosome, Allele_A, and Allele_B info.
SNP.anno <- read.csv("SNP_annotation.csv", header=TRUE)
SNP.list <- as.character(SNP.anno[,1])
SNP.anno[,1] <- sapply(SNP.list, SNP_list_parse)
ADC.meth.filenames <- list.files("D:/Phil/betareg/data/meth_genes/ADC_meth_genes/", full.names=TRUE)
ADC.SNP.filenames <- list.files("D:/Phil/betareg/data/ADC_SNP_data_clean", full.names=TRUE)
SCC.meth.filenames <- list.files("D:/Phil/betareg/data/meth_genes/SCC_meth_genes/", full.names=TRUE)
SCC.SNP.filenames <- list.files("D:/Phil/betareg/data/SCC_SNP_data_clean", full.names=TRUE)
#This chunk creates a list of genes whose corresponding data is used in the regression loop.
#Some genes have no methylation data (i.e. no probes were identifed as methylated) and must be removed.
ADC.meth.genes <- sapply(ADC.meth.filenames, meth_filename_split)
ADC.SNP.genes <- sapply(ADC.SNP.filenames, SNP_filename_split)
names(ADC.meth.genes) <- NULL
names(ADC.SNP.genes) <- NULL
ADC.genes <- intersect(ADC.meth.genes, ADC.SNP.genes)
#37 genes in the ADC dataset have both methylation and SNP data.
SCC.meth.genes <- sapply(SCC.meth.filenames, meth_filename_split)
SCC.SNP.genes <- sapply(SCC.SNP.filenames, SNP_filename_split)
names(SCC.meth.genes) <- NULL
names(SCC.SNP.genes) <- NULL
SCC.genes <- intersect(SCC.meth.genes, SCC.SNP.genes)
#35 genes in the ADC dataset have both methylation and SNP data.
#Remove patients with NA > 0.10, Afro > 0.10.
setwd("D:/Phil/adjusted_betareg/data")
ADC.covariates <- read.csv("ADC_total_covariates.csv", stringsAsFactors=FALSE)
SCC.covariates <- read.csv("SCC_total_covariates.csv", stringsAsFactors=FALSE)
ADC.rm.IDs <- ADC.covariates$patient.ID[unique(c(which(ADC.covariates$proportion.african > 0.10), which(ADC.covariates$proportion.native.amer > 0.10)))]
SCC.rm.IDs <- SCC.covariates$patient.ID[unique(c(which(SCC.covariates$proportion.african > 0.10), which(SCC.covariates$proportion.native.amer > 0.10)))]
#genes <- ADC.genes
#hist <- "ADC"
#rm.IDs <- ADC.rm.IDs
#The function below runs beta regression on data subsets.
#Takes as parameters a list of genes, and a histology.
#The outer loops iterates over the methylation probes and the inner loop iterates ovet the SNP probes.
#Outputs a file for each iteration of the loop.
beta_reg_rm_NA_afro <- function(genes, hist, rm.IDs){
for (l in 1:length(genes)){
#This chunk sets up the regression table.
current.gene <- genes[l]
current.hist <- hist
if (current.hist == "ADC"){
meth.file <- ADC.meth.filenames[which(ADC.meth.genes == current.gene)]
SNP.file <- ADC.SNP.filenames[which(ADC.SNP.genes == current.gene)]
}else if (current.hist == "SCC"){
meth.file <- SCC.meth.filenames[which(SCC.meth.genes == current.gene)]
SNP.file <- SCC.SNP.filenames[which(SCC.SNP.genes == current.gene)]
}
meth.data <- read.csv(meth.file, header=TRUE) #first four columns are not BVs
names(meth.data)[3] <- "Patient.ID"
#remove IDs
rm.ind <- match(rm.IDs, meth.data$patient.name)
rm.ind <- rm.ind[which(is.na(rm.ind) == FALSE)]
meth.data <- meth.data[-rm.ind, ]
SNP.data <- read.csv(SNP.file, header=TRUE)
#Merge on Patient.ID
reg.table <- merge(meth.data[,-c(1,2,4)], SNP.data[,-c(1,3)])
rownames(reg.table) <- reg.table[,1]
reg.table <- as.matrix(reg.table[,-1])
meth.probes <- names(meth.data)[-(1:4)]
SNP.probes <-names(SNP.data)[-(1:3)]
N <- length(meth.probes)
M <- length(SNP.probes)
k <- 1
reg.results.table <- as.data.frame(matrix(0,N*M,9))
names(reg.results.table) <- c("Gene", "Probe", "SNP", "Estimate", "P.Value", "Prevalence", "Test.Allele", "ADC.or.SCC", "Sample.Size")
for (i in 1:N){
meth.probe.name <- meth.probes[i]
for (j in 1:M){
SNP.probe.name <- SNP.probes[j]
Beta_Value <- reg.table[,i]
SNP <- reg.table[,j+N]
a.freq <- calc_allele_freq(SNP)
test.allele <- as.character(SNP.anno$Allele_B[match(SNP.probe.name, SNP.anno$probe_ID)])
reg.results.table$Prevalence[k] <- a.freq
reg.results.table$Sample.Size[k] <- nrow(reg.table)
reg.results.table$Test.Allele[k] <- test.allele
reg.results.table$Gene[k] <- current.gene
reg.results.table$ADC.or.SCC[k] <- current.hist
reg.results.table$Probe[k] <- meth.probe.name
reg.results.table$SNP[k] <- SNP.probe.name
if ((a.freq == 1)|(a.freq == 0)){
reg.results.table$Estimate[k] <- NA
reg.results.table$P.Value[k] <- NA
k <- k+1
next
}
model <- betareg(Beta_Value ~ SNP, link="logit")
reg.results.table$Estimate[k] <- model$coefficients$mean[2]
reg.results.table$P.Value[k] <- summary(model)$coefficients$mean[2,4]
k <- k+1
}
}
directory <- paste0("D:/Phil/betareg/output/", current.hist, "_", "reg_results_unadjusted_no_NA_afro/")
filename <- paste0(current.hist, "_reg_results_", current.gene, ".csv")
write.csv(reg.results.table, paste0(directory, filename), row.names=FALSE)
}
}
ptm <- proc.time()
beta_reg_rm_NA_afro(ADC.genes, hist="ADC", rm.IDs=ADC.rm.IDs)
ADC.runtime <- proc.time() - ptm
ADC.runtime
#ADC
#user system elapsed
#1322.65 0.10 1323.84
ptm <- proc.time()
beta_reg_rm_NA_afro(SCC.genes, hist="SCC", rm.IDs=SCC.rm.IDs)
SCC.runtime <- proc.time() - ptm
SCC.runtime
#SCC.runtime
#user system elapsed