plotMD.DGEList: Mean-Difference Plot of Count Data

Description

Creates a mean-difference plot (aka MA plot) with color coding for highlighted points.

Usage

## S3 method for class 'DGEList':
plotMD(object, column = 1, xlab = "Average log CPM (this sample and others)",
       ylab = "log-ratio (this sample vs others)",
       main = colnames(object)[column], status=object$genes$Status,
       zero.weights = FALSE, prior.count = 3, ...)
## S3 method for class 'DGEGLM':
plotMD(object, column = ncol(object), coef = NULL, xlab = "Average log CPM",
       ylab = "log-fold-change", main = colnames(object)[column],
       status=object$genes$Status, zero.weights = FALSE, ...)
## S3 method for class 'DGELRT':
plotMD(object, xlab = "Average log CPM",
       ylab = "log-fold-change", main = object$comparison,
       status=object$genes$Status, \dots)

Arguments

object

an object of class DGEList, DGEGLM, DGEGLM or DGEExact.

column

integer, column of object to be plotted.

coef

alternative to column for fitted model objects. If specified, then column is ignored.

xlab

character string, label for x-axis

ylab

character string, label for y-axis

main

character string, title for plot

status

vector giving the control status of each spot on the array, of same length as the number of rows of object. If NULL, then all points are plotted in the default color, symbol and size.

zero.weights

logical, should spots with zero or negative weights be plotted?

prior.count

the average prior count to be added to each observation. Larger values produce more shrinkage.

...

other arguments are passed to plotWithHighlights.

Value

A plot is created on the current graphics device.

Details

A mean-difference plot (MD-plot) is a plot of log fold changes (differences) versus average log values (means). The history of mean-difference plots and MA-plots is reviewed in Ritchie et al (2015). For DGEList objects, a between-sample MD-plot is produced. Counts are first converted to log2-CPM values. An articifial array is produced by averaging all the samples other than the sample specified. A mean-difference plot is then producing from the specified sample and the artificial sample. This procedure reduces to an ordinary mean-difference plot when there are just two arrays total. If object is an DGEGLM object, then the plot is an fitted model MD-plot in which the estimated coefficient is on the y-axis and the average logCPM value is on the x-axis. If object is an DGEExact or DGELRT object, then the MD-plot displays the logFC vs the logCPM values from the results table. The status vector can correspond to any grouping of the probes that is of interest. If object is a fitted model object, then status vector is often used to indicate statistically significance, so that differentially expressed points are highlighted. The status can be included as the component object$genes$Status instead of being passed as an argument to plotMD. See plotWithHighlights for how to set colors and graphics parameters for the highlighted and non-highlighted points.

References