Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object

SCANORAMA is analogous to computer vision algorithms for panorama stitching that identify images with overlapping content and merge these into a larger panorama.

runSCANORAMA(
  inSCE,
  useAssay = "logcounts",
  batch = "batch",
  SIGMA = 15,
  ALPHA = 0.1,
  KNN = 20L,
  assayName = "SCANORAMA"
)

Arguments

inSCE

SingleCellExperiment inherited object. Required.

useAssay

A single character indicating the name of the assay requiring batch correction. Scanorama requires a transformed normalized expression assay. Default "logcounts".

batch

A single character indicating a field in colData that annotates the batches. Default "batch".

SIGMA

A numeric scalar. Algorithmic parameter, correction smoothing parameter on Gaussian kernel. Default 15.

ALPHA

A numeric scalar. Algorithmic parameter, alignment score minimum cutoff. Default 0.1.

KNN

An integer. Algorithmic parameter, number of nearest neighbors to use for matching. Default 20L.

assayName

A single characeter. The name for the corrected assay. Will be saved to assay. Default "SCANORAMA".

Value

The input SingleCellExperiment object with assay(inSCE, assayName) updated.

References

Brian Hie et al, 2019

Examples

if (FALSE) {
data('sceBatches', package = 'singleCellTK')
sceBatches <- scaterlogNormCounts(sceBatches)
sceCorr <- runSCANORAMA(sceBatches, "ScaterLogNormCounts")
}