plotExprsFreqVsMean {scater}R Documentation

Plot frequency against mean for each feature

Description

Plot the frequency of expression (i.e., percentage of expressing cells) against the mean expression level for each feature in a SingleCellExperiment object.

Usage

plotExprsFreqVsMean(object, freq_exprs, mean_exprs, controls,
  by_show_single = FALSE, show_smooth = TRUE, show_se = TRUE, ...)

Arguments

object

A SingleCellExperiment object.

freq_exprs

Specification of the row-level metadata field containing the number of expressing cells per feature, see ?"scater-vis-var" for possible values. Note that only metadata fields will be searched, assays will not be used. If not supplied or NULL, this defaults to "n_cells_by_counts" or equivalent for compacted data.

mean_exprs

Specification of the row-level metadata field containing the mean expression of each feature, see ?"scater-vis-var" for possible values. Again, only metadata fields will be searched, assays will not be used. If not supplied or NULL, this defaults to "mean_counts" or equivalent for compacted data.

controls

Specification of the row-level metadata column indicating whether a feature is a control, see ?"scater-vis-var" for possible values. Only metadata fields will be searched, assays will not be used. If not supplied, this defaults to "is_feature_control" or equivalent for compacted data.

by_show_single

Logical scalar specifying whether a single-level factor for controls should be used for colouring, see ?"scater-vis-var" for details.

show_smooth

Logical scalar, should a smoothed fit (through feature controls if available; all features otherwise) be shown on the plot? See geom_smooth for details.

show_se

Logical scalar, should the standard error be shown for a smoothed fit?

...

Further arguments passed to plotRowData.

Details

This function plots gene expression frequency versus mean expression level, which can be useful to assess the effects of technical dropout in the dataset. We fit a non-linear least squares curve for the relationship between expression frequency and mean expression. We use this curve to define the number of genes above high technical dropout and the numbers of genes that are expressed in at least 50% and at least 25% of cells.

The plot will attempt to colour the points based on whether the corresponding features are labelled as feature controls in object. This can be turned off by setting controls=NULL.

Value

A ggplot object.

See Also

plotRowData

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)

example_sce <- calculateQCMetrics(example_sce, 
    feature_controls = list(set1 = 1:500))
plotExprsFreqVsMean(example_sce)

plotExprsFreqVsMean(example_sce, size_by = "is_feature_control")


[Package scater version 1.8.4 Index]