| calculateAverage {scater} | R Documentation |
Calculate average counts per feature, adjusting them to account for normalization due to size factors or library sizes.
calculateAverage(object, exprs_values = "counts", use_size_factors = TRUE, subset_row = NULL, BPPARAM = SerialParam()) calcAverage(object, exprs_values = "counts", use_size_factors = TRUE, subset_row = NULL, BPPARAM = SerialParam())
object |
A SingleCellExperiment object or count matrix. |
exprs_values |
A string specifying the assay of |
use_size_factors |
a logical scalar specifying whetherthe size factors in |
subset_row |
A vector specifying the subset of rows of |
BPPARAM |
A BiocParallelParam object specifying whether the calculations should be parallelized. |
The size-adjusted average count is defined by dividing each count by the size factor and taking the average across cells. All sizes factors are scaled so that the mean is 1 across all cells, to ensure that the averages are interpretable on the scale of the raw counts.
Assuming that object is a SingleCellExperiment:
If use_size_factors=TRUE, size factors are automatically extracted from the object.
Note that different size factors may be used for features marked as spike-in controls.
This is due to the presence of control-specific size factors in object, see normalizeSCE for more details.
If use_size_factors=FALSE, all size factors in object are ignored.
Size factors are instead computed from the library sizes, using librarySizeFactors.
If use_size_factors is a numeric vector, it will override the any size factors for non-spike-in features in object.
The spike-in size factors will still be used for the spike-in transcripts.
If no size factors are available, they will be computed from the library sizes using librarySizeFactors.
If object is a matrix or matrix-like object, size factors can be supplied by setting use_size_factors to a numeric vector.
Otherwise, the sum of counts for each cell is used as the size factor through librarySizeFactors.
Vector of average count values with same length as number of features, or the number of features in subset_row if supplied.
data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
list(counts = sc_example_counts),
colData = sc_example_cell_info)
## calculate average counts
ave_counts <- calculateAverage(example_sce)