runPerCellQC: Wrapper for calculating QC metrics with scater.
In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description Usage Arguments Value Examples

A wrapper function for decontX. Identify potential contamination from experimental factors such as ambient RNA.

runPerCellQC(
  inSCE,
  useAssay = "counts",
  collectionName = NULL,
  geneSetList = NULL,
  geneSetListLocation = "rownames",
  geneSetCollection = NULL,
  percent_top = c(50, 100, 200, 500),
  use_altexps = FALSE,
  flatten = TRUE,
  detectionLimit = 0,
  BPPARAM = BiocParallel::SerialParam()
)

`inSCE`	Input SingleCellExperiment object.
`useAssay`	A string specifying which assay in the SCE to use. Default `"counts"`.
`collectionName`	Character. Name of a `GeneSetCollection` obtained by using one of the importGeneSet* functions. Default `NULL`.
`geneSetList`	List of gene sets to be quantified. The genes in the assays will be matched to the genes in the list based on `geneSetListLocation`. Default `NULL`.
`geneSetListLocation`	Character or numeric vector. If set to 'rownames', then the genes in 'geneSetList' will be looked up in `rownames(inSCE)`. If another character is supplied, then genes will be looked up in the column names of `rowData(inSCE)`. A character vector with the same length as `geneSetList` can be supplied if the IDs for different gene sets are found in different places, including a mixture of 'rownames' and `rowData(inSCE)`. An integer or integer vector can be supplied to denote the column index in `rowData(inSCE)`. Default 'rownames'.
`geneSetCollection`	Class of `GeneSetCollection` from package `GSEAbase`. The location of the gene IDs in `inSCE` should be in the `description` slot of each gene set and should follow the same notation as `geneSetListLocation`. The function getGmt can be used to read in gene sets from a GMT file. If reading a GMT file, the second column for each gene set should be the description denoting the location of the gene IDs in `inSCE`. These gene sets will be included with those from `geneSetList` if both parameters are provided.
`percent_top`	An integer vector. Each element is treated as a number of top genes to compute the percentage of library size occupied by the most highly expressed genes in each cell.
`use_altexps`	Logical scalar indicating whether QC statistics should be computed for alternative Experiments in x. If TRUE, statistics are computed for all alternative experiments. Alternatively, an integer or character vector specifying the alternative Experiments to use to compute QC statistics. Alternatively NULL, in which case alternative experiments are not used.
`flatten`	Logical scalar indicating whether the nested DataFrame-class in the output should be flattened.
`detectionLimit`	A numeric scalar specifying the lower detection limit for expression.
`BPPARAM`	A BiocParallelParam object specifying whether the QC calculations should be parallelized.

A SingleCellExperiment object with cell QC metrics added to the colData slot. If geneSetList or geneSetCollection are provided, then the rownames for each gene set will be saved in metadata(inSCE)$scater$addPerCellQC$geneSets.

data(scExample, package = "singleCellTK")
mito.ix = grep("^MT-", rowData(sce)$feature_name)
geneSet <- list("Mito"=rownames(sce)[mito.ix])
sce <- runPerCellQC(sce, geneSetList = geneSet)