Plot heatmap of missing values

Create a heatmap of the data matrix, indicating which values are missing and observed, respectively.

plotMissingValuesHeatmap(
  sce,
  assayMissing,
  onlyRowsWithMissing = FALSE,
  settings = "clustered",
  ...
)

Arguments

sce

A SummarizedExperiment object.

assayMissing

Character scalar indicating the name of a logical assay of sce representing the missingness pattern. FALSE entries should represent observed values, while TRUE entries represent missing values.

onlyRowsWithMissing

Logical scalar indicating whether to only include rows with at least one missing (TRUE) value.

settings

Character scalar or NULL. Setting this to "clustered" creates a heatmap with rows and columns clustered (used in the einprot report). Setting it to NULL allows any argument to be passed to ComplexHeatmap::Heatmap via the ... argument.

...

Additional arguments passed to ComplexHeatmap::Heatmap.

Value

A ComplexHeatmap object.

Author

Charlotte Soneson

Examples

sce <- importExperiment(system.file("extdata", "mq_example",
                                    "1356_proteinGroups.txt",
                                    package = "einprot"),
                        iColPattern = "^iBAQ\\.")$sce
SummarizedExperiment::assay(sce, "iBAQ")[
    SummarizedExperiment::assay(sce, "iBAQ") == 0] <- NA
SummarizedExperiment::assay(sce, "missing") <-
    is.na(SummarizedExperiment::assay(sce, "iBAQ"))
plotMissingValuesHeatmap(sce, "missing")