voom_ihs.R

library(limma)
voom_ihs <- function (counts, design = NULL, lib.size = NULL, normalize.method = "none", 
          span = 0.5, plot = FALSE, save.plot = FALSE, ...) 
{
    ihs <- function(x) {
        transformed <- log(x + sqrt(x ^ 2 + 1))
        return(transformed)
    }
    
    hs <- function(x) {
        y <- 0.5*exp(-x)*(exp(2*x)-1)
        return(y)
    }
    
    out <- list()
    if (is(counts, "DGEList")){
        out$genes <- counts$genes
        out$targets <- counts$samples
        if (is.null(design) && diff(range(as.numeric(counts$sample$group))) > 
            0) 
            design <- model.matrix(~group, data = counts$samples)
        if (is.null(lib.size)) 
            lib.size <- with(counts$samples, lib.size * norm.factors)
        counts <- counts$counts
    }else{
        isExpressionSet <- suppressPackageStartupMessages(is(counts, 
                                                             "ExpressionSet"))
        if (isExpressionSet) {
            if (length(Biobase::fData(counts))) 
                out$genes <- Biobase::fData(counts)
            if (length(Biobase::pData(counts))) 
                out$targets <- Biobase::pData(counts)
            counts <- Biobase::exprs(counts)
        }
        else {
            counts <- as.matrix(counts)
        }
    }
    
    n <- nrow(counts)
    
    if (n < 2L){stop("Need at least two genes to fit a mean-variance trend")}
    
    if (is.null(design)) {
        design <- matrix(1, ncol(counts), 1)
        rownames(design) <- colnames(counts)
        colnames(design) <- "GrandMean"
    }
    
    if (is.null(lib.size)){lib.size <- colSums(counts)}
    
    y <- t(ihs(t(counts)/lib.size))
    
    y <- normalizeBetweenArrays(y, method = normalize.method)
    
    fit <- lmFit(y, design, ...)
    
    if (is.null(fit$Amean)){fit$Amean <- rowMeans(y, na.rm = TRUE)} 
    
    #   fitted mean for each row (taxa)    
    sx <- fit$Amean 
    #   fitted standard deviation
    sy <- sqrt(fit$sigma)
    
    allzero <- rowSums(counts) == 0
    
    if (any(allzero)) {
        sx <- sx[!allzero]
        sy <- sy[!allzero]
    }
    
    l <- lowess(sx, sy, f = span)
    
    if (plot){
        plot(sx, sy, xlab = "mean(ihs(count/sizeFactor))", ylab = "Sqrt(standard deviation )", 
             pch = 16, cex = 0.25)
        title("voom_ihs: Mean-variance trend")
        lines(l, col = "red")
    }
    
    #   linearly interpolate given the smooth line from the lowess
    f <- approxfun(l, rule = 2)
    
    if(fit$rank < ncol(design)){
        j <- fit$pivot[1:fit$rank]
        fitted.values <- fit$coef[, j, drop = FALSE] %*% t(fit$design[, 
                                                                      j, drop = FALSE])
    }else{
        fitted.values <- fit$coef %*% t(fit$design)
    }
    
    fitted.cpm <- hs(fitted.values)
    
    fitted.count <- t(t(fitted.cpm)*(lib.size))
    
    fitted.ihs <- ihs(fitted.count)
    
    w <- 1/f(fitted.ihs)^4
    
    dim(w) <- dim(fitted.ihs)
    
    out$E <- y
    
    out$weights <- w
    
    out$design <- design
    
    if(is.null(out$targets)){
        out$targets <- data.frame(lib.size = lib.size)
    }else{
        out$targets$lib.size <- lib.size
        }
    
    if (save.plot) {
        out$voom.xy <- list(x = sx, y = sy, xlab = "mean(ihs(count/sizeFactor))", 
                            ylab = "Sqrt( standard deviation )")
        out$voom.line <- l
    }
    
    new("EList", out)
}