R/runBatchCorrection.R
runMNNCorrect.Rd
MNN is designed for batch correction of single-cell RNA-seq data where the batches are partially confounded with biological conditions of interest. It does so by identifying pairs of MNN in the high-dimensional log-expression space. For each MNN pair, a pairwise correction vector is computed by applying a Gaussian smoothing kernel with bandwidth `sigma`.
runMNNCorrect(
inSCE,
useAssay = "logcounts",
batch = "batch",
assayName = "MNN",
k = 20L,
propK = NULL,
sigma = 0.1,
cosNormIn = TRUE,
cosNormOut = TRUE,
varAdj = TRUE,
BPPARAM = BiocParallel::SerialParam()
)
Input SingleCellExperiment object
A single character indicating the name of the assay requiring
batch correction. Default "logcounts"
.
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"
.
A single characeter. The name for the corrected assay. Will
be saved to assay
. Default
"MNN"
.
An integer scalar specifying the number of nearest neighbors to
consider when identifying MNNs. See "See Also". Default 20
.
A numeric scalar in (0, 1) specifying the proportion of cells in
each dataset to use for mutual nearest neighbor searching. See "See Also".
Default NULL
.
A numeric scalar specifying the bandwidth of the Gaussian
smoothing kernel used to compute the correction vector for each cell. See
"See Also". Default 0.1
.
A logical scalar indicating whether cosine normalization
should be performed on the input data prior to calculating distances between
cells. See "See Also". Default TRUE
.
A logical scalar indicating whether cosine normalization
should be performed prior to computing corrected expression values. See "See
Also". Default TRUE
.
A logical scalar indicating whether variance adjustment should
be performed on the correction vectors. See "See Also". Default TRUE
.
A BiocParallelParam object specifying whether the PCA and nearest-neighbor searches should be parallelized.
The input SingleCellExperiment object with
assay(inSCE, assayName)
updated.
Haghverdi L, Lun ATL, et. al., 2018