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", col_by_variable="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 1.6.3 Index]