plotExplanatoryVariables {scater} | R Documentation

## Plot explanatory variables ordered by percentage of variance explained

### Description

Plot explanatory variables ordered by percentage of variance explained

### Usage

    plotExplanatoryVariables(object, nvars_to_plot = 10,
      min_marginal_r2 = 0, theme_size = 10, ...)

### Arguments

`object` | A SingleCellExperiment object containing expression values and experimental information. Alternatively, a matrix containing the output of `getVarianceExplained`.
`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.
`min_marginal_r2` | Numeric scalar specifying the minimal value required for median marginal R-squared for a variable to be plotted. Only variables with a median marginal R-squared strictly larger than this value will be plotted.
`theme_size` | Numeric scalar specifying the font size to use for the plotting theme
`...` | Parameters to be passed to `getVarianceExplained`.

### Details

A density plot is created for each variable, showing the distribution of R-squared across all genes. Only the `nvars_to_plot` variables with the largest median R-squared across genes are shown. Variables are also only shown if they have median R-squared values above `min_marginal_r2`.

If `object` is a SingleCellExperiment object, `getVarianceExplained` will be called to compute the variance in expression explained by each variable in each gene. Users may prefer to run `getVarianceExplained` 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)

    plotExplanatoryVariables(example_sce)

[Package scater version 1.12.2 Index]