% Generated by roxygen2: do not edit by hand % Please edit documentation in R/runBatchCorrection.R \name{runSCANORAMA} \alias{runSCANORAMA} \title{Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object} \usage{ runSCANORAMA( inSCE, useAssay = "logcounts", batch = "batch", assayName = "SCANORAMA", SIGMA = 15, ALPHA = 0.1, KNN = 20, approx = TRUE ) } \arguments{ \item{inSCE}{Input \linkS4class{SingleCellExperiment} object} \item{useAssay}{A single character indicating the name of the assay requiring batch correction. Scanorama requires a transformed normalized expression assay. Default \code{"logcounts"}.} \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{assayName}{A single characeter. The name for the corrected assay. Will be saved to \code{\link{assay}}. Default \code{"SCANORAMA"}.} \item{SIGMA}{A numeric scalar. Algorithmic parameter, correction smoothing parameter on Gaussian kernel. Default \code{15}.} \item{ALPHA}{A numeric scalar. Algorithmic parameter, alignment score minimum cutoff. Default \code{0.1}.} \item{KNN}{An integer. Algorithmic parameter, number of nearest neighbors to use for matching. Default \code{20}.} \item{approx}{Boolean. Use approximate nearest neighbors, greatly speeds up matching runtime. Default \code{TRUE}.} } \value{ The input \linkS4class{SingleCellExperiment} object with \code{assay(inSCE, assayName)} updated. } \description{ SCANORAMA is analogous to computer vision algorithms for panorama stitching that identify images with overlapping content and merge these into a larger panorama. } \examples{ \dontrun{ data('sceBatches', package = 'singleCellTK') logcounts(sceBatches) <- log1p(counts(sceBatches)) sceCorr <- runSCANORAMA(sceBatches, "ScaterLogNormCounts") } } \references{ Brian Hie et al, 2019 }