R/IMA2.sitetest.R

IMA2.sitetest <- function (dataf, gcase = "g2", gcontrol = "g1", testmethod = c("wilcox", "limma", "pooled", "satterthwaite"), Padj = "BH", concov = "OFF",
                      rawpcut = NULL, adjustpcut = NULL, betadiffcut = NULL, paired = FALSE, nCPU = NULL, verbose = TRUE) {
    beta <- dataf@bmatrix
    group <- dataf@groupinfo
    grouplev <- group[, 2]
    if (concov == "ON") {
        cat("Performing linear regression....\n")
        if (is.null(nCPU)) {
            nCPU <- floor(detectCores()/2)
        } else {}
        pheno <- as.numeric(as.character(grouplev))
        assoc <- function (betaBlock, pheno) {
            testout <- t(apply(betaBlock, 1, function (x) {
                temp <- summary(lm(x ~ pheno))
                pvalue <- temp$coefficients[2, c(1, 4)]
                return(pvalue)
            }))
        }
        nCpGs <- nrow(beta)
        remainder <- nCpGs%%nCPU
        quotient <- (nCpGs-remainder)/nCPU
        indexes <- seq(0, nCpGs, by = quotient)
        indexes[nCPU+1] <- nCpGs
        allAssoc <- do.call("rbind", mclapply(seq_len(nCPU), mc.cores = nCPU, function (i) {
            assoc(beta[(indexes[i]+1):indexes[i+1], ], pheno)
        }))
        adjustP <- p.adjust(allAssoc[, 2], method = Padj)
        out <- cbind(allAssoc[, 2], adjustP, allAssoc[, 1])
        rownames(out) <- rownames(beta)
        colnames(out) <- c("P-Value", "Adjust Pval", "Coefficient")
    } else {
        caseind <- which(grouplev %in% gcase)
        controlind <- which(grouplev %in% gcontrol)
        if (paired == TRUE) {
            lev1 <- caseind[order(group[caseind, 3])]
            lev2 <- controlind[order(group[controlind, 3])]
        } else {
            lev1 <- caseind
            lev2 <- controlind
        }
        eset <- beta[, c(lev1, lev2)]
        if (testmethod == "wilcox") {
            cat("Performing Wilcox testing...\n")
            testout <- apply(eset, 1, function (x) {
                wilcox.test(x[seq_along(lev1)], x[(length(lev1) + 1):(length(lev1) + length(lev2))], paired = paired)$p.value
            })
        } else {}
        if (testmethod == "limma") {
            cat("Performing limma...\n")
            TS <- as.factor(c(rep("T", length(lev1)), rep("C", length(lev2))))
            SS <- rep(seq_along(lev1), 2)
            if (paired == FALSE) {
                design <- model.matrix(~0 + TS)
                rownames(design) <- colnames(eset)
                colnames(design) <- c("C", "T")
                fit <- lmFit(eset, design)
                cont.matrix <- makeContrasts(comp = T - C, levels = design)
                fit2 <- contrasts.fit(fit, cont.matrix)
                fit2 <- eBayes(fit2)
                result1 <- topTable(fit2, coef = 1, adjust.method = Padj, number = nrow(fit2))
            }  else {
                design <- model.matrix(~SS + TS)
                colnames(design) <- c("Intercept", "Pairorder", "TST")
                rownames(design) <- colnames(eset)
                cat("Here is your design matrix\n")
                print(design)
                fit <- lmFit(eset, design)
                fit2 <- eBayes(fit)
                result1 <- topTable(fit2, coef = "TST", adjust.method = Padj, number = nrow(fit2))
            }
            testout <- result1[match(rownames(eset), rownames(result1)), "P.Value"]
        }
        if (testmethod == "pooled") {
            cat("Performing pooled t.test...\n")
            testout <- apply(eset, 1, function (x) {
                t.test(x[seq_along(lev1)], x[(length(lev1) + 1):(length(lev1) + length(lev2))], var.equal = TRUE, paired = paired)$p.value
            })
        }
        if (testmethod == "satterthwaite") {
            cat("Performing satterthwaite t.test...\n")
            testout <- apply(eset, 1, function (x) {
                t.test(x[seq_along(lev1)], x[(length(lev1) + 1):(length(lev1) + length(lev2))], paired = paired)$p.value
            })
        }
        adjustP <- p.adjust(testout, method = Padj)
        difb <- apply(eset, 1, function (x) {
            mean(x[seq_along(lev1)]) - mean(x[(length(lev1) + 1):ncol(eset)])
        })
        out <- cbind(testout, adjustP, difb, rowMeans(eset[, seq_along(lev1)]), rowMeans(eset[, (length(lev1) + 1):ncol(eset)]))
        rownames(out) <- rownames(eset)
        colnames(out) <- c("P-Value", "Adjust Pval", "Beta-Difference",
                        paste("Mean", paste(gcase, collapse = "_"), sep = "_"),
                        paste("Mean", paste(gcontrol, collapse = "_"), sep = "_"))
    }
    outputDMfunc <- function(out, rawpcut = 0.05, adjustpcut = 0.05, betadiffcut = 0.14) {
        if (is.null(rawpcut) & is.null(adjustpcut) & is.null(betadiffcut)) {
            cat("Kept the full comparison result. Please specify the significance criteria if you are only interested in the differentially methylated regions/sites only\n")
            return(out)
        } else {
            if (!is.null(rawpcut)) {
                rawpcutout = out[, 1] <= rawpcut
            } else {
                rawpcutout = out[, 1] <= 1
            }
            if (!is.null(adjustpcut)) {
                adjustpcutout = out[, 2] <= adjustpcut
            } else {
               adjustpcutout = out[, 2]<=1
            }
            if (!is.null(betadiffcut)) {
               betadiffcutout = abs(out[, 3])>=betadiffcut
            } else {
               betadiffcutout = abs(out[, 3]) >= 0
            }
            return(out[(rawpcutout & adjustpcutout & betadiffcutout), ])
       }
    }
    out <- outputDMfunc(out = out, rawpcut = rawpcut, adjustpcut = adjustpcut, betadiffcut = betadiffcut)
    return(out)
}
mcanouil/IMA2 documentation built on May 22, 2019, 12:59 p.m.