Calculate quality control metrics

Usage

calculateMetrics(object, ...)

# S4 method for class 'Matrix'
calculateMetrics(object, rowData = NULL, prefilter = FALSE)

# S4 method for class 'SummarizedExperiment'
calculateMetrics(object, assay = 1L, prefilter = FALSE)

# S4 method for class 'matrix'
calculateMetrics(object, rowData = NULL, prefilter = FALSE)

Arguments

object: Object.
rowData: DataFrame. Metadata describing the assay rows, if genomic ranges are not available. Use rowRanges (GenomicRanges) instead, if possible.
prefilter: logical(1). Drop very low quality samples/cells from the object. This can resize the number of columns but the rows (i.e. features) do not change with this operation.
assay: vector(1). Assay name or index position.
...: Additional arguments.

Value

matrix / Matrix: DFrame containing metrics.
SummarizedExperiment: Modified object, with metrics in colData().

Details

Input a raw count matrix. Do not use size factor adjusted or log normalized counts here.

Note

Updated 2021-09-11.

Author

Michael Steinbaugh, Rory Kirchner

Examples

data(RangedSummarizedExperiment, package = "AcidTest")

## SummarizedExperiment ====
object <- RangedSummarizedExperiment
names(SummarizedExperiment::colData(object))
#> [1] "condition"
object <- calculateMetrics(object)
#> → Calculating 12 sample metrics.
#> ℹ 497 coding features detected.
#> ℹ 0 mitochondrial features detected.
names(SummarizedExperiment::colData(object))
#> [1] "condition"             "nCount"                "nFeature"             
#> [4] "nCoding"               "nMito"                 "log10FeaturesPerCount"
#> [7] "mitoRatio"