plot.summary.subsamples

0th

Percentile

plot metrics as a function of subsampled read depth

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 'summary.subsamples':
plot(x, ...)
Arguments
x
a summary.subsamples object
...
further arguments passed to or from other methods.
Value

  • see description

Aliases
  • plot.summary.subsamples
Examples
if (interactive()) {
  # import the subsampling object (see ?subsample to see how ss is created)
  data(ss)

  # summarise object
  ss <- summary(ss)

  # plot
  plot(ss)
}
Documentation reproduced from package subSeq, version 1.2.2, License: MIT + file LICENSE

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