Apply BBKNN batch effect correction method to SingleCellExperiment object

Source: R/runBatchCorrection.R

BBKNN, an extremely fast graph-based data integration algorithm. It modifies the neighbourhood construction step to produce a graph that is balanced across all batches of the data.

runBBKNN(
  inSCE,
  useAssay = "logcounts",
  batch = "batch",
  reducedDimName = "BBKNN",
  nComponents = 50L
)

Arguments

inSCE

Input SingleCellExperiment object

useAssay

A single character indicating the name of the assay requiring batch correction. Default "logcounts".

batch

A single character indicating a field in colData that annotates the batches of each cell; or a vector/factor with the same length as the number of cells. Default "batch".

reducedDimName

A single character. The name for the corrected low-dimensional representation. Will be saved to reducedDim(inSCE). Default "BBKNN".

nComponents

An integer. Number of principle components or the dimensionality, adopted in the pre-PCA-computation step, the BBKNN step (for how many PCs the algorithm takes into account), and the final UMAP combination step where the value represent the dimensionality of the updated reducedDim. Default 50L.

Value

The input SingleCellExperiment object with reducedDim(inSCE, reducedDimName) updated.

References

Krzysztof Polanski et al., 2020

Examples

if (FALSE) {
data('sceBatches', package = 'singleCellTK')
logcounts(sceBatches) <- log1p(counts(sceBatches))
sceBatches <- runBBKNN(sceBatches, useAssay = "logcounts",
                       nComponents = 10)
}