normalizeData {CSSQ} | R Documentation |
This function iterates over kmeansNormalize
to perform
normalization for all samples in the dataset. It returns an
RangedSummarizedExperiment-class
object normalized counts, cluster information and the variance of that
cluster for that sample.
normalizeData(ansData, numClusters = 4)
ansData |
|
numClusters |
A number indicating the number of clusters to use for k-means clustering. (default: 4) |
RangedSummarizedExperiment-class
containing the
normalized counts, cluster information and the variance of the cluster in
the sample.
kmeansNormalize
which this function calls.
exRange <- GRanges(seqnames=c("chr1","chr2","chr3","chr4"), ranges=IRanges(start=c(1000,2000,3000,4000),end=c(1500,2500,3500,4500))) sampleInfo <- read.table(system.file("extdata", "sample_info.txt", package="CSSQ",mustWork = TRUE),sep="\t",header=TRUE) exCount <- matrix(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16),nrow=4,ncol=4) exData <- SummarizedExperiment(assays = list(ansCount=exCount), rowRanges=exRange,colData=sampleInfo) normExData <- normalizeData(exData,numClusters=2) assays(normExData)$normCount