R/vsMAMatrix.R

In btmonier/vidger-nd: Create rapid visualizations of RNAseq data in R

#' @title 
#' MA plot matrix from log2 fold changes and -log10(p-values)
#'   
#' @author 
#' Brandon Monier, \email{brandon.monier@sdstate.edu}
#' 
#' @description
#' This function allows you to generate MA plots for all possible treatment
#' combinations for a given factor in either a cuffdiff, DESeq2, or edgeR
#' data set.
#'  
#' @param data output generated from calling the main routines of either
#'  `cuffdiff`, `DESeq2`, or `edgeR` analyses. For `cuffdiff`, this will be a 
#'  `*_exp.diff` file. For `DESeq2`, this will be a generated object of class 
#'  `DESeqDataSet`. For `edgeR`, this will be a generated object of class 
#'  `DGEList`.
#' @param d.factor a specified factor; for use with `DESeq2` objects only.
#'  This input equates to the first parameter for the contrast argument when 
#'  invoking the `results()` function in `DESeq2`. Defaults to `NULL`.
#' @param type an analysis classifier to tell the function how to process the 
#'  data. Must be either `cuffdiff`, `deseq`, or `edger`. `cuffdiff` must be 
#'  used with `cuffdiff` data; `deseq` must be used for `DESeq2` output; 
#'  `edgeR` must be used with `edgeR` data. See the `data` parameter for 
#'  further details.
#' @param padj a user defined adjusted p-value cutoff point. 
#'  Defaults to `0.05`.
#' @param y.lim set manual limits (boundaries) to the y axis. Defaults to 
#'  `NULL`.
#' @param lfc log fold change level for setting conditonals. If no user input 
#'  is added (`NULL`), value defaults to `1`.
#' @param title display the main title of plot. Logical; defaults to `TRUE`.
#'  If set to `FALSE`, no title will display in plot.
#' @param legend display legend of plot. Logical; defaults to `TRUE`.
#'  If set to `FALSE`, no legend will display in plot.
#' @param grid display major and minor axis lines. Logical; defaults to `TRUE`.
#'  If set to `FALSE`, no axis lines will display in plot.
#' @param counts displays the number of differentially expressed genes for 
#'  each treatment comparison. Defaults to `TRUE`.
#' @param data.return returns data output of plot Logical; defaults to `FALSE`.
#'  If set to `TRUE`, a data frame will also be called. Assign to object
#'  for reproduction and saving of data frame. See final example for further
#'  details.
#' 
#' @return An object created by \code{ggplot}
#' 
#' @export
#' 
#' @examples
#' # Cuffdiff example
#' data("df.cuff")
#' vsMAMatrix(
#'  data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
#'  padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
#'  grid = TRUE, counts = TRUE, data.return = FALSE
#' )
#' 
#' # DESeq2 example
#' data("df.deseq")
#' require(DESeq2)
#' vsMAMatrix(
#'  data = df.deseq, d.factor = 'condition', type = 'deseq', 
#'  padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
#'  grid = TRUE, counts = TRUE, data.return = FALSE
#' )
#' 
#' # edgeR example
#' data("df.edger")
#' require(edgeR)
#' vsMAMatrix(
#'  data = df.edger, d.factor = NULL, type = 'edger', 
#'  padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
#'  grid = TRUE, counts = TRUE, data.return = FALSE
#' )
#'                 
#' # Extract data frame from visualization
#' data("df.cuff")
#' tmp <- vsMAMatrix(
#'  data = df.cuff, d.factor = NULL, 
#'  type = 'cuffdiff', padj = 0.05, y.lim = NULL,
#'  lfc = 1, title = TRUE, grid = TRUE, 
#'  counts = TRUE, data.return = TRUE
#' )
#' df.vmat <- tmp[[1]]
#' head(df.vmat)

vsMAMatrix <- function(
    data, d.factor = NULL, type = c("cuffdiff", "deseq", "edger"), 
    padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, 
    grid = TRUE, counts = TRUE, data.return = FALSE
) {
    if (missing(type) || !type %in% c("cuffdiff", "deseq", "edger")) {
        stop('Please specify analysis type ("cuffdiff", "deseq", or "edger")')
    }
    
    type <- match.arg(type)
    if(type == 'cuffdiff'){
        dat <- .getCuffMAMatrix(data)
    } else if (type == 'deseq') {
        dat <- .getDeseqMAMatrix(data, d.factor)
    } else if (type == 'edger') {
        dat <- .getEdgeMAMatrix(data)
    }

    if (!isTRUE(title)) {
        m.lab <- NULL
    } else {
        m.lab <- ggtitle('MA Matrix')
    }

    if (!isTRUE(legend)) {
        leg <- theme(legend.position = 'none')
    } else {
        leg <- guides(colour = guide_legend(override.aes = list(size = 3)))
    }
    
    if (!isTRUE(grid)) {
        grid <- theme_classic()
    } else {
        grid <- theme_bw()
    }

    dat$isDE <- ifelse(dat$padj <= padj, TRUE, FALSE)
    py <- dat$M
    
    if (is.null(y.lim)) {
        y.lim = c(-1, 1) * quantile(abs(py[is.finite(py)]), probs = 0.99) * 0.8
    }
    if (is.null(lfc)) {
        lfc = 1
    }

    dat <- .ma.ranker(dat, padj, lfc, y.lim)
    
    tmp.size <- .ma.out.ranker(py, y.lim[2])
    tmp.col <- .ma.col.ranker(dat$isDE, py, lfc)
    tmp.shp <- .ma.shp.ranker(py, y.lim)
    
    tmp.cnt <- .ma.col.counter(dat, lfc)
    b <- tmp.cnt[[1]]
    g <- tmp.cnt[[2]]
    
    comp1 <- .ma.comp1(y.lim, padj, lfc, b, g)
    point <- geom_point(
        alpha = 0.7, 
        aes(color = tmp.col, shape = tmp.shp, size = tmp.size)
    )
    comp2 <- .ma.comp2(
        comp1[[4]], comp1[[6]], comp1[[5]], comp1[[1]], comp1[[2]], comp1[[3]]
    )

    tmp.l <- .mamat.col.count(dat)

    if (isTRUE(counts)) {
        b.count <- annotate(
            'text', 
            x = -Inf, 
            y = Inf, 
            vjust = 1.5, 
            hjust = -1, 
            label = tmp.l$blue$Freq, 
            color = 'royalblue1', 
            fontface = 2
        )
        g.count <- annotate(
            'text', 
            x = Inf, 
            y = Inf, 
            vjust = 1.5, 
            hjust = 1.5, 
            label = tmp.l$green$Freq, 
            color = 'green', 
            fontface = 2
        )
    } else {
        b.count <- NULL
        g.count <- NULL
    }

    M <- A <- pval <- color <- size <- shape <- NULL
    tmp.plot <- ggplot(
        dat, aes(x = A, y = pmax(y.lim[1], pmin(y.lim[2], py)))
    ) +
        point + 
        comp2$shape + 
        comp2$color + 
        comp2$size + 
        comp1$hline1 +
        comp1$hline2 +
        comp1$hline3 +
        comp1$x.lab +
        comp1$y.lab +
        m.lab +
        grid +
        leg +
        b.count +
        g.count +
        ylim(y.lim) +
        facet_wrap(id_x ~ id_y)
    
    if (isTRUE(data.return)) {
        dat2 <- dat[, -ncol(dat)]
        plot.l <- list(data = dat2, plot = tmp.plot)
    } else {
        print(tmp.plot)
    }
}