sumCountsAcrossCells {scater} | R Documentation |
Sum together expression values (by default, counts) for each set of cells and for each feature.
sumCountsAcrossCells(x, ...) aggregateAcrossCells(x, ...) ## S4 method for signature 'ANY' sumCountsAcrossCells(x, ids, subset_row = NULL, subset_col = NULL, average = FALSE, BPPARAM = SerialParam()) ## S4 method for signature 'SummarizedExperiment' sumCountsAcrossCells(x, ..., exprs_values = "counts") ## S4 method for signature 'SummarizedExperiment' aggregateAcrossCells(x, ids, ..., use_exprs_values = "counts") ## S4 method for signature 'SingleCellExperiment' aggregateAcrossCells(x, ids, ..., subset_row = NULL, use_exprs_values = "counts", use_altexps = TRUE)
x |
For For |
... |
For the generics, further arguments to be passed to specific methods. For the For |
ids |
A factor specifying the set to which each cell in Alternatively, a DataFrame of such vectors or factors, in which case each unique combination of levels defines a set. |
subset_row |
An integer, logical or character vector specifying the features to use. Defaults to all features. For the SingleCellExperiment method, this argument will not affect alternative Experiments,
where summation is always performed for all features (or not at all, depending on |
subset_col |
An in teger, logical or character vector specifying the cells to use.
Defaults to all cells with non- |
average |
Logical scalar indicating whether the average should be computed instead of the sum. |
BPPARAM |
A BiocParallelParam object specifying whether summation should be parallelized. |
exprs_values |
A string or integer scalar specifying the assay of |
use_exprs_values |
A character or integer vector specifying the assay(s) of |
use_altexps |
Logical scalar indicating whether aggregation should be performed for alternative experiments in Alternatively, a character vector specifying the names of the alternative experiments to be aggregated. |
This function provides a convenient method for aggregating counts across multiple columns for each feature. A typical application would be to sum counts across all cells in each cluster to obtain “pseudo-bulk” samples for further analysis.
The behaviour of this function is equivalent to that of colsum
.
However, this function can operate on any matrix representation in object
;
can do so in a parallelized manner for large matrices without resorting to block processing;
and can natively support combinations of multiple factors in ids
.
Any NA
values in ids
are implicitly ignored and will not be considered during summation.
This may be useful, e.g., to remove undesirable cells by setting their entries in ids
to NA
.
Alternatively, we can explicitly select the cells of interest with subset_col
.
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.
For sumCountsAcrossCells
with a factor ids
, a count matrix is returned with one column per level of ids
.
For each feature, counts for all cells in the same set are summed together.
Columns are ordered by levels(ids)
.
For sumCountsAcrossCells
with a DataFrame ids
, a SummarizedExperiment is returned containing a similar count matrix in the first assay.
Each column corresponds to a unique combination of levels in ids
and contains the sum of counts for all cells with that combination.
The identities of the levels for each column are reported in the colData
.
For aggregateAcrossCells
, a SummarizedExperiment of the same class as x
is returned,
containing summed matrices generated by sumCountsAcrossCell
on all assays specified by use_exprs_values
.
Column metadata is retained for the first instance of a cell from each set in ids
.
If ids
is a DataFrame, the combination of levels corresponding to each column is also reported in the column metadata.
Aaron Lun
example_sce <- mockSCE() ids <- sample(LETTERS[1:5], ncol(example_sce), replace=TRUE) out <- sumCountsAcrossCells(example_sce, ids) head(out) batches <- sample(1:3, ncol(example_sce), replace=TRUE) out2 <- sumCountsAcrossCells(example_sce, DataFrame(label=ids, batch=batches)) out2