#' @title
#' MA plot from mean expression and log fold changes from different analytical
#' objects
#'
#' @author
#' Brandon Monier, \email{brandon.monier@sdstate.edu}
#'
#' @description
#' This function allows you to extract necessary results-based data from
#' different output files to create a MA plot (i.e. a scatter plot) of log2
#' fold changes versus normalized mean counts while implementing ggplot2
#' aesthetics.
#'
#' @param x treatment `x` for comparison (log2(x/control)). This will be a
#' factor level in your data.
#' @param y treatment `y` for comparison (log2(y/control)). This will be a
#' factor level in your data.
#' @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 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")
#' vsMAPlot(
#' x = 'hESC', y = 'iPS', data = df.cuff, d.factor = NULL,
#' type = 'cuffdiff', padj = 0.05, y.lim = NULL, lfc = 1,
#' title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE
#' )
#'
#' # DESeq2 example
#' data("df.deseq")
#' require(DESeq2)
#' vsMAPlot(
#' x = 'treated_paired.end', y = 'untreated_paired.end',
#' data = df.deseq, d.factor = 'condition', type = 'deseq',
#' padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE,
#' legend = TRUE, grid = TRUE, data.return = FALSE
#' )
#'
#' # edgeR example
#' data("df.edger")
#' require(edgeR)
#' vsMAPlot(
#' x = 'WM', y = 'MM', data = df.edger, d.factor = NULL,
#' type = 'edger', padj = 0.1, y.lim = NULL, lfc = 1,
#' title = FALSE, legend = TRUE, grid = TRUE, data.return = FALSE
#' )
#'
#' # Extract data frame from visualization
#' data("df.cuff")
#' tmp <- vsMAPlot(
#' x = 'hESC', y = 'iPS', data = df.cuff,
#' d.factor = NULL, type = 'cuffdiff', padj = 0.05,
#' y.lim = NULL, lfc = 1, title = TRUE, grid = TRUE,
#' data.return = TRUE
#' )
#' df.ma <- tmp[[1]]
#' head(df.ma)
vsMAPlot <- function(
x, y, data, d.factor = NULL, type = c("cuffdiff", "deseq", "edger"),
padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE,
grid = 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 <- .getCuffMA(x, y, data)
} else if (type == 'deseq') {
dat <- .getDeseqMA(x, y, data, d.factor)
} else if (type == 'edger') {
dat <- .getEdgeMA(x, y, data)
}
if (!isTRUE(title)) {
m.lab <- NULL
} else {
m.lab <- ggtitle(paste(y, 'vs.', x))
}
if (!isTRUE(legend)) {
leg <- theme(legend.position = 'none')
} else {
leg <- guides(colour = guide_legend(override.aes = list(size = 3)),
shape = 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$M
if (is.null(y.lim)) {
y.lim = c(-1.5, 1.5) * quantile(abs(py[is.finite(py)]), probs = 0.99)
}
if (is.null(lfc)) {
lfc = 1
}
dat <- .ma.ranker(dat, padj, lfc, y.lim)
tmp.size <- .ma.out.ranker(py, y.lim[2])
tmp.col <- .ma.col.ranker(dat$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]]
)
A <- NULL
tmp.plot <- ggplot(
dat, aes(x = A, y = pmax(y.lim[1], pmin(y.lim[2], py)))
) +
point +
comp2$color + comp2$shape + comp1$hline1 + comp1$hline2 +
comp1$hline3 + comp1$x.lab + comp1$y.lab + m.lab + ylim(y.lim) +
comp2$size + grid + leg
if (isTRUE(data.return)) {
dat2 <- dat[, -ncol(dat)]
plot.l <- list(data = dat2, plot = tmp.plot)
} else {
print(tmp.plot)
}
}
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