numDetectedAcrossFeatures {scuttle} | R Documentation |
Computes the number of detected expression values (by default, defined as non-zero counts) for each group of features in each cell.
numDetectedAcrossFeatures(x, ...) ## S4 method for signature 'ANY' numDetectedAcrossFeatures( x, ids, subset.row = NULL, subset.col = NULL, average = FALSE, threshold = 0, BPPARAM = SerialParam(), subset_row = NULL, subset_col = NULL, detection_limit = NULL ) ## S4 method for signature 'SummarizedExperiment' numDetectedAcrossFeatures(x, ..., assay.type = "counts", exprs_values = NULL)
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 generic, further arguments to pass to specific methods. For the SummarizedExperiment method, further arguments to pass to the ANY method. |
ids |
A factor of length 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- |
average |
Logical scalar indicating whether the proportion of non-zero counts in each group should be computed instead. |
threshold |
A numeric scalar specifying the threshold above which a gene is considered to be detected. |
BPPARAM |
A BiocParallelParam object specifying whether summation should be parallelized. |
subset_row |
Soft-deprecated equivalents of the arguments described above. |
subset_col |
Soft-deprecated equivalents of the arguments described above. |
assay.type |
A string or integer scalar specifying the assay of |
exprs_values, detection_limit |
Soft-deprecated equivalents of the arguments above. |
An integer matrix containing the number of detected expression values in each group of features (row) and cell (column).
If average=TRUE
, this is instead a numeric matrix containing the proportion of detected values.
Aaron Lun
sumCountsAcrossFeatures
, on which this function is based.
example_sce <- mockSCE() ids <- sample(paste0("GENE_", 1:100), nrow(example_sce), replace=TRUE) byrow <- numDetectedAcrossFeatures(example_sce, ids) head(byrow[,1:10])