plotQC {scater}R Documentation

Produce QC diagnostic plots

Description

Produce QC diagnostic plots

Usage

plotQC(object, type = "highest-expression", ...)

Arguments

object

an SingleCellExperiment object containing expression values and experimental information. Must have been appropriately prepared.

type

character scalar providing type of QC plot to compute: "highest-expression" (showing features with highest expression), "find-pcs" (showing the most important principal components for a given variable), "explanatory-variables" (showing a set of explanatory variables plotted against each other, ordered by marginal variance explained), or "exprs-mean-vs-freq" (plotting the mean expression levels against the frequency of expression for a set of features).

...

arguments passed to plotHighestExprs, findImportantPCs, plotExplanatoryVariables and {plotExprsMeanVsFreq} as appropriate.

Details

Display useful quality control plots to help with pre-processing of data and identification of potentially problematic features and cells.

Value

a ggplot plot 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)

drop_genes <- apply(exprs(example_sce), 1, function(x) {var(x) == 0})
example_sce <- example_sce[!drop_genes, ]
example_sce <- calculateQCMetrics(example_sce)
plotQC(example_sce, type="high", colour_cells_by="Mutation_Status")
plotQC(example_sce, type="find", variable="total_features")
vars <- names(colData(example_sce))[c(2:3, 5:14)]
plotQC(example_sce, type="expl", variables=vars)


[Package scater version 1.8.4 Index]