% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/scanpyFunctions.R
 \name{runScanpyNormalizeData}
 \alias{runScanpyNormalizeData}
 \title{runScanpyNormalizeData
 Wrapper for NormalizeData() function from scanpy library

 Normalizes the sce object according to the input parameters}

 \usage{
 runScanpyNormalizeData(
   inSCE,
   useAssay,

   targetSum = 10000,

   maxFraction = 0.05,

   normAssayName = "scanpyNormData"

 )
 }
 \arguments{
 \item{inSCE}{(sce) object to normalize}
 
 \item{useAssay}{Assay containing raw counts to use for normalization.}
 

 \item{targetSum}{If NULL, after normalization, each observation (cell) has a 
 total count equal to the median of total counts for observations (cells) 

 before normalization. Default \code{1e4}}

 \item{maxFraction}{Include cells that have more counts than max_fraction of 
 the original total counts in at least one cell. Default \code{0.05}}

 \item{normAssayName}{Name of new assay containing normalized data. Default
 \code{scanpyNormData}.}
 }
 \value{
 Normalized \code{SingleCellExperiment} object
 }
 \description{
 runScanpyNormalizeData
 Wrapper for NormalizeData() function from scanpy library

 Normalizes the sce object according to the input parameters

 }
 \examples{
 data(scExample, package = "singleCellTK")

 sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
 rownames(sce) <- rowData(sce)$feature_name

 \dontrun{
 sce <- runScanpyNormalizeData(sce, useAssay = "counts")
 }
 }