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logreg_phil_unadjusted_no_NA_afro_cutoff1_case_controls_too.R
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logreg_phil_unadjusted_no_NA_afro_cutoff1_case_controls_too.R
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#Calculate cutoff by taking average of beta values in pooled normal + 0.2
#Less than cutoff is unmethylated, greater than is methylated.
#logistic regression
#If you have these SNPs what are the odds a particular probe is methylated?
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)
}
setwd("D:/Phil/adjusted_betareg/data")
unadjusted.reg.results <- read.csv("sig_reg_results_no_NA_afro.csv", stringsAsFactors=FALSE)
results.table <- unadjusted.reg.results[which(unadjusted.reg.results$ADC.or.SCC == "ADC"), ]
names(results.table)[7] <- "OR.Estimate"
N <- nrow(results.table)
results.table$OR.Estimate <- rep(0, N)
results.table$P.Value <- rep(0, N)
results.table$Prevalence <- rep(0, N)
results.table$Sample.Size <- rep(0, N)
results.table$ADC.or.SCC <- rep("", N)
sig.probes <- unique(unadjusted.reg.results$Meth.Probe.Name)
sig.SNPs <- unique(unadjusted.reg.results$SNP.ID)
sig.SNPs <- sapply(sig.SNPs, SNP_list_parse, USE.NAMES=FALSE)
setwd("D:/Phil/betareg/data")
ADC.meth.data <- read.csv("ADC_total_sig_meth_data.csv", stringsAsFactors=FALSE)
SCC.meth.data <- read.csv("SCC_total_sig_meth_data.csv", stringsAsFactors=FALSE)
#Calculate cutoffs
STN.meth.data <- read.csv("find_methylation_status_STN_data.csv", stringsAsFactors=FALSE)
STN.meth.data <- STN.meth.data[ , match(sig.probes, names(STN.meth.data))]
meth.cutoffs1 <- colMeans(STN.meth.data) + 0.2
#These cutoffs were also tested, the one above was actually used.
meth.cutoffs2 <- rep(0.25, length(sig.probes))
names(meth.cutoffs2) <- sig.probes
ADC.meth.cutoffs3 <- apply(ADC.meth.data[-(1:2)], 2, median)
SCC.meth.cutoffs3 <- apply(SCC.meth.data[-(1:2)], 2, median)
#Recode methylation data
dichotomize_meth_data <- function(meth.data, cutoffs){
N <- ncol(meth.data)
for (i in 3:N){
cutoff <- cutoffs[match(names(meth.data)[i], names(cutoffs))]
meth.data[which(meth.data[i] < cutoff), i] <- 0
meth.data[which(meth.data[i] >= cutoff), i] <- 1
}
return(meth.data)
}
ADC.meth.data.dicho <- dichotomize_meth_data(ADC.meth.data, meth.cutoffs1)
SCC.meth.data.dicho <- dichotomize_meth_data(SCC.meth.data, meth.cutoffs1)
#This chunk creates case control tables.
create_case_control_table <- function(reg.table){
N <- nrow(reg.table)
case.control.dfrm$Cases <- colSums(reg.table)
case.control.dfrm$Controls <- N - case.control.dfrm$Cases
}
setwd("D:/Phil/adjusted_betareg/data")
ADC.SNP.data <- read.csv("ADC_SNP_data_no_27k.csv", stringsAsFactors=FALSE)
SCC.SNP.data <- read.csv("SCC_SNP_data_no_27k.csv", stringsAsFactors=FALSE)
ADC.SNP.data <- ADC.SNP.data[, c(1,2,match(sig.SNPs, names(ADC.SNP.data)))]
SCC.SNP.data <- SCC.SNP.data[, c(1,2,match(sig.SNPs, names(SCC.SNP.data)))]
M <- length(sig.probes)
case.control.table <- data.frame("Meth.Probe" = sig.probes, "Controls"=rep(0,M), "Cases" = rep(0,M))
#meth.data <- ADC.meth.data.dicho
#SNP.data <- ADC.SNP.data
#reg.results.table <- results.table
#hist <- "ADC"
logistic_reg <- function(meth.data, SNP.data, reg.results.table, hist, case.control.dfrm){
#This chunk sets up results table.
N <- nrow(reg.results.table)
reg.results.table$ADC.or.SCC<- rep(hist, N)
for (i in 1:N){
meth.probe <- reg.results.table$Meth.Probe.Name[i]
SNP.ID <- reg.results.table$SNP.ID[i]
SNP.ID <- SNP_list_parse(SNP.ID)
test.pair.ind <- list(match(meth.probe, names(meth.data)), match(SNP.ID, names(SNP.data)))
if (any(is.na(unlist(test.pair.ind) == TRUE))){
reg.results.table$OR.Estimate[i] <- NA
reg.results.table$P.Value[i] <- NA
next
}
meth.binary <- meth.data[test.pair.ind[[1]]]
row.names(meth.binary) <- meth.data$patient.name
SNPx <- SNP.data[test.pair.ind[[2]]]
row.names(SNPx) <- SNP.data$patient.ID
reg.table <- merge(meth.binary, SNPx, by="row.names")
probe.ind <- match(meth.probe, case.control.dfrm$Meth.Probe)
case.control.dfrm$Cases[probe.ind] <- sum(reg.table[2])
case.control.dfrm$Controls[probe.ind] <- nrow(reg.table) - sum(reg.table[2])
reg.results.table$Sample.Size[i] <- nrow(reg.table)
a.freq <- calc_allele_freq(reg.table[3])
reg.results.table$Prevalence[i] <- a.freq
if ((a.freq == 1)|(a.freq == 0)){
reg.results.table$OR.Estimate[i] <- NA
reg.results.table$P.Value[i] <- NA
next
}
Meth_Binary <- unlist(reg.table[2], use.names=FALSE)
SNP <- unlist(reg.table[3], use.names=FALSE)
model <- glm(Meth_Binary ~ SNP, family=binomial("logit"))
reg.results.table$OR.Estimate[i] <- exp(coef(model))[2]
reg.results.table$P.Value[i] <- summary(model)$coefficients[2,4]
}
return(list(reg.results.table, case.control.dfrm))
}
x <- logistic_reg(dichotomize_meth_data(ADC.meth.data, meth.cutoffs1), ADC.SNP.data, results.table, "ADC", case.control.table)
ADC.logreg.results <- x[[1]]
ADC.case.controls <- x[[2]]
y <- logistic_reg(dichotomize_meth_data(SCC.meth.data, meth.cutoffs1), SCC.SNP.data, results.table, "SCC", case.control.table)
SCC.logreg.results <- y[[1]]
SCC.case.controls <- y[[2]]
setwd("D:/Phil/betareg/output/")
z <- rbind(ADC.logreg.results, SCC.logreg.results)
w <- cbind(rbind(ADC.case.controls, SCC.case.controls), "Histology" = c(rep("ADC", M), rep("SCC", M)))
u <- as.data.frame(t(meth.cutoffs1))
write.csv(z, "unadjusted_logreg_results_cutoff1.csv", row.names=FALSE)
write.csv(w, "unadjusted_logreg_case_controls_cutoff1.csv", row.names=FALSE)
write.csv(u, "logreg_cutoffs1.csv", row.names=FALSE)