plotExplanatoryPCs {scater} | R Documentation

## Plot the explanatory PCs for each variable

### Description

Plot the explanatory PCs for each variable

### Usage

    plotExplanatoryPCs(object, nvars_to_plot = 10, npcs_to_plot = 50,
      theme_size = 10, ...)

### Arguments

`object` | A SingleCellExperiment object containing expression values and experimental information. Alternatively, a matrix containing the output of `getExplanatoryPCs`.
`nvars_to_plot` | Integer scalar specifying the number of variables with the greatest explanatory power to plot. This can be set to `Inf` to show all variables.
`npcs_to_plot` | Integer scalar specifying the number of PCs to plot.
`theme_size` | numeric scalar providing base font size for ggplot theme.
`...` | Parameters to be passed to `getExplanatoryPCs`.

### Details

A density plot is created for each variable, showing the R-squared for each successive PC (up to `npcs_to_plot` PCs). Only the `nvars_to_plot` variables with the largest maximum R-squared across PCs are shown.

If `object` is a SingleCellExperiment object, `getExplanatoryPCs` will be called to compute the variance in expression explained by each variable in each gene. Users may prefer to run `getExplanatoryPCs` manually and pass the resulting matrix as `object`, in which case the R-squared values are used directly.

### Value

A ggplot object.

### Examples

    data("sc_example_counts")
    data("sc_example_cell_info")
    example_sce <- SingleCellExperiment(
        assays = list(counts = sc_example_counts),
        colData = sc_example_cell_info)
    example_sce <- normalize(example_sce)

    plotExplanatoryPCs(example_sce)

[Package scater version 1.12.2 Index]