% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/runBatchCorrection.R

 \name{runFastMNN}
 \alias{runFastMNN}

 \title{Apply a fast version of the mutual nearest neighbors (MNN) batch effect

 correction method to SingleCellExperiment object}
 \usage{

 runFastMNN(
   inSCE,

   useAssay = "logcounts",

   useReducedDim = NULL,

   batch = "batch",

   reducedDimName = "fastMNN",
   k = 20,
   propK = NULL,
   ndist = 3,
   minBatchSkip = 0,
   cosNorm = TRUE,
   nComponents = 50,
   weights = NULL,
   BPPARAM = BiocParallel::SerialParam()

 )

 }
 \arguments{

 \item{inSCE}{Input \linkS4class{SingleCellExperiment} object}

 \item{useAssay}{A single character indicating the name of the assay requiring

 batch correction. Default \code{"logcounts"}.}
 
 \item{useReducedDim}{A single character indicating the dimension reduction
 used for batch correction. Will ignore \code{useAssay} when using.
 Default \code{NULL}.}
 
 \item{batch}{A single character indicating a field in \code{colData} that
 annotates the batches of each cell; or a vector/factor with the same length
 as the number of cells. Default \code{"batch"}.}

 \item{reducedDimName}{A single character. The name for the corrected

 low-dimensional representation. Default \code{"fastMNN"}.}
 
 \item{k}{An integer scalar specifying the number of nearest neighbors to
 consider when identifying MNNs. See "See Also". Default \code{20}.}
 
 \item{propK}{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 \code{NULL}.}

 \item{ndist}{A numeric scalar specifying the threshold beyond which
 neighbours are to be ignored when computing correction vectors. See "See
 Also". Default \code{3}.}

 \item{minBatchSkip}{Numeric scalar specifying the minimum relative magnitude
 of the batch effect, below which no correction will be performed at a given
 merge step. See "See Also". Default \code{0}.}
 
 \item{cosNorm}{A logical scalar indicating whether cosine normalization
 should be performed on \code{useAssay} prior to PCA. See "See Also". Default
 \code{TRUE}.}
 
 \item{nComponents}{An integer scalar specifying the number of dimensions to
 produce. See "See Also". Default \code{50}.}
 
 \item{weights}{The weighting scheme to use. Passed to
 \code{\link[batchelor]{multiBatchPCA}}. Default \code{NULL}.}
 
 \item{BPPARAM}{A \linkS4class{BiocParallelParam} object specifying whether
 the SVD should be parallelized.}

 }
 \value{

 The input \linkS4class{SingleCellExperiment} object with
 \code{reducedDim(inSCE, reducedDimName)} updated.

 }
 \description{

 fastMNN is a variant of the classic MNN method, modified for speed and more
 robust performance. For introduction of MNN, see \code{\link{runMNNCorrect}}.

 }
 \examples{
 data('sceBatches', package = 'singleCellTK')

 logcounts(sceBatches) <- log1p(counts(sceBatches))
 sceCorr <- runFastMNN(sceBatches, useAssay = 'logcounts')

 }

 \references{
 Lun ATL, et al., 2016
 }

 \seealso{
 \code{\link[batchelor]{fastMNN}} for using \code{useAssay}, and
 \code{\link[batchelor]{reducedMNN}} for using \code{useReducedDim}
 }