runBBKNN: Apply BBKNN batch effect correction method to...

In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description

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.

Usage

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

Arguments

inSCE	SingleCellExperiment inherited object. Required.
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. 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

## Not run: 
data('sceBatches', package = 'singleCellTK')
sceCorr <- runBBKNN(sceBatches)

## End(Not run)

singleCellTK documentation built on Nov. 8, 2020, 5:21 p.m.