plotRPC {celda}R Documentation

Visualize perplexity differences of a list of celda models

Description

Visualize perplexity differences of every model in a celdaList, by unique K/L combinations. Line represents centered moving average with windows of length n.

Usage

plotRPC(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5)

## S4 method for signature 'SingleCellExperiment'
plotRPC(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5)

## S4 method for signature 'celdaList'
plotRPC(x, sep = 1, n = 10, alpha = 0.5)

Arguments

x

Can be one of

  • A SingleCellExperiment object returned from celdaGridSearch, recursiveSplitModule, or recursiveSplitCell. Must contain a list named "celda_grid_search" in metadata(x).

  • celdaList object.

altExpName

The name for the altExp slot to use. Default "featureSubset".

sep

Numeric. Breaks in the x axis of the resulting plot.

n

Integer. Width of the rolling window. Default 10.

alpha

Numeric. Passed to geom_jitter. Opacity of the points. Values of alpha range from 0 to 1, with lower values corresponding to more transparent colors.

Value

A ggplot plot object showing perplexity diferences as a function of clustering parameters.

Examples

data(sceCeldaCGGridSearch)
sce <- resamplePerplexity(sceCeldaCGGridSearch)
plotRPC(sce, n = 1)
data(celdaCGSim, celdaCGGridSearchRes)
## Run various combinations of parameters with 'celdaGridSearch'
celdaCGGridSearchRes <- resamplePerplexity(
  celdaCGSim$counts,
  celdaCGGridSearchRes)
plotRPC(celdaCGGridSearchRes, n = 1)

[Package celda version 1.9.3 Index]