R/plot.prepGOglm.R

In gu-mi/GOglm: Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression

###########################################################################
## generic function plot() and new methods for different classes
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# for prepGOglm class, define method plot.prepGOglm():

##' Plot DE test results (from the \code{\link{prepare}} function)
##'
##' @title Plot Two Histograms For Significance Statistics and Gene Lengths
##'
##' @param x An object of class \code{prepGOglm}
##'
##' @param ... Other parameters (for future use)
##'
##' @return A panel of two histograms for significance statistics and
##' (transformed) gene lengths, respectively.
##'
##' @method plot prepGOglm
##' @rdname plot.prepGOglm
##' @export
##'
##' @author Gu Mi \email{mig@@stat.oregonstate.edu}, Yanming Di
##' \email{diy@@stat.oregonstate.edu}
##'
##' @seealso \code{\link{plot}}
##'
##' @examples
##' ## Load the datasets into R session:
##' data(ProsCan_DE)
##' DE_data <- ProsCan_DE
##' data(ProsCan_Length)
##' Length_data <- ProsCan_Length
##'
##' ## Prepare a data frame of class "prepGOglm":
##' gene_table <- prepare(DE_data, Length_data, trans.p = "d.log", trans.l = TRUE)
##'
##' ## Call the generic plot() function:
##' plot(gene_table)
##'
plot.prepGOglm <- function(x, ...){
    par(mfrow=c(1,2))
    hist(x[,1], main="Histogram of Siginificance Statistics",
         xlab="Significance Statistics")
    hist(x[,2], main="Historgram of Gene Lengths",
         xlab="Gene Length (in bp)")
}