sumCountsAcrossFeatures: Sum counts across feature sets

In LTLA/scuttle: Single-Cell RNA-Seq Analysis Utilities

Sum counts across feature sets

Description

Sum together expression values (by default, counts) for each feature set in each cell.

Usage

sumCountsAcrossFeatures(x, ...)

## S4 method for signature 'ANY'
sumCountsAcrossFeatures(
  x,
  ids,
  subset.row = NULL,
  subset.col = NULL,
  average = FALSE,
  BPPARAM = SerialParam(),
  subset_row = NULL,
  subset_col = NULL
)

## S4 method for signature 'SummarizedExperiment'
sumCountsAcrossFeatures(x, ..., assay.type = "counts", exprs_values = NULL)

Arguments

x	A numeric matrix of counts containing features in rows and cells in columns. Alternatively, a SummarizedExperiment object containing such a count matrix.
...	For the sumCountsAcrossFeatures generic, further arguments to be passed to specific methods. For the SummarizedExperiment method, further arguments to be passed to the ANY method.
ids	A factor of length nrow(x), specifying the set to which each feature in x belongs. Alternatively, a list of integer or character vectors, where each vector specifies the indices or names of features in a set. Logical vectors are also supported.
subset.row	An integer, logical or character vector specifying the features to use. Defaults to all features.
subset.col	An integer, logical or character vector specifying the cells to use. Defaults to all cells with non-NA entries of ids.
average	Logical scalar indicating whether the average should be computed instead of the sum.
BPPARAM	A BiocParallelParam object specifying whether summation should be parallelized.
subset_row, subset_col, exprs_values	Soft-deprecated equivalents of the arguments described above.
assay.type	A string or integer scalar specifying the assay of x containing the matrix of counts (or any other expression quantity that can be meaningfully summed).

Details

This function provides a convenient method for aggregating counts across multiple rows for each cell. Several possible applications are listed below:

• Using a list of genes in ids, we can obtain a summary expression value for all genes in one or more gene sets. This allows the activity of various pathways to be compared across cells.

• Genes with multiple mapping locations in the reference will often manifest as multiple rows with distinct Ensembl/Entrez IDs. These counts can be aggregated into a single feature by setting the shared identifier (usually the gene symbol) as ids.

• It is theoretically possible to aggregate transcript-level counts to gene-level counts with this function. However, it is often better to do so with dedicated functions (e.g., from the tximport or tximeta packages) that account for differences in length across isoforms.

The behaviour of this function is equivalent to that of rowsum. However, this function can operate on any matrix representation in object, and can do so in a parallelized manner for large matrices without resorting to block processing.

If ids is a factor, any NA values are implicitly ignored and will not be considered or reported. This may be useful, e.g., to remove undesirable feature sets by setting their entries in ids to NA.

Setting average=TRUE will compute the average in each set rather than the sum. This is particularly useful if x contains expression values that have already been normalized in some manner, as computing the average avoids another round of normalization to account for differences in the size of each set.

Value

A count matrix is returned with one row per level of ids. In each cell, counts for all features in the same set are summed together (or averaged, if average=TRUE). Rows are ordered according to levels(ids).

Author(s)

Aaron Lun

See Also

aggregateAcrossFeatures, to perform additional aggregation of row-level metadata.

numDetectedAcrossFeatures, to compute the number of detected features per cell.

Examples

example_sce <- mockSCE()
ids <- sample(LETTERS, nrow(example_sce), replace=TRUE)
out <- sumCountsAcrossFeatures(example_sce, ids)
str(out)

LTLA/scuttle documentation built on March 9, 2024, 11:16 a.m.