plot.subsamples: plot metrics as a function of subsampled read depth

In StoreyLab/subSeq: Subsampling of high-throughput sequencing count data

Description

Plot the number of genes found significant, the Spearman correlation of the effect size estimates with the full experiment, and the empirical false discovery rate as a function of the subsampled read depth. This determines whether these metrics saturate, which indicates that the experiment has an appropriate sequencing depth.

Usage

## S3 method for class 'subsamples'
plot(x, ...)

Arguments

x	a subsamples object
...	further arguments passed to or from other methods.

Details

This is an alias for the plot.summary.subsamples function, so that plotting can be done directly on the subsamples object. We recommend using summary(ss) first, so that the summary operation does not have to be performed each time the figure is plotted, and so the summary object can be examined on its own.

Value

plot a subSeq object

Examples

if (interactive()) {
# import the subsampling object (see ?subsample to see how ss is created)
data(ss)

# plot subsample object
plot(ss)
}

StoreyLab/subSeq documentation built on June 4, 2019, 12:09 a.m.