simulatorZ (version 1.6.0)

geneFilter: geneFilter

Description

the function to filter genes by Intergrative Correlation

Usage

geneFilter(obj, cor.cutoff = 0.5)

Arguments

obj

a list of ExpressionSet, matrix or RangedSummarizedExperiment objects. If

its elements are matrices, columns represent samples, rows represent genes

cor.cutoff

the cutoff threshold for filtering genes. Only when the integrative correlation

between every pair of sets is larger than the cutoff value, will the gene

be selected.

Value

objects with genes filtered

References

Garrett-Mayer, E., Parmigiani, G., Zhong, X., Cope, L.,

Gabrielson, E., Cross-study validation and combined analysis of gene

expression microarray data. Biostatistics. 2008 Apr;9(2):333-354.

Examples

set.seed(8)


library(curatedOvarianData)


library(GenomicRanges)


data(GSE17260_eset)


data(E.MTAB.386_eset)


data(GSE14764_eset)


esets <- list(GSE17260=GSE17260_eset, E.MTAB.386=E.MTAB.386_eset, GSE14764=GSE14764_eset)


esets.list <- lapply(esets, function(eset){


  return(eset[1:1500, 1:10])


})





result.set <- geneFilter(esets.list, 0)


result.set


### as we cannot calculate correlation with one set, this function just 


### delivers the same set if esets has length 1


result.oneset <- geneFilter(esets.list[1])


result.oneset





## Support matrices


X.list <- lapply(esets.list, function(eset){


  return(exprs(eset)) ## Columns represent samples!


})


result.set <- geneFilter(X.list, 0)


dim(result.set[[1]])





## Support RangedSummarizedExperiment


nrows <- 200; ncols <- 6


counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)


rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),


                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),


                     strand=sample(c("+", "-"), 200, TRUE))


colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),


                     row.names=LETTERS[1:6])


sset <- SummarizedExperiment(assays=SimpleList(counts=counts),


                             rowRanges=rowRanges, colData=colData)


s.list <- list(sset, sset)


result.set <- geneFilter(s.list, 0.9) 


## the same set should resemble each other, no genes filtered


dim(assay(result.set[[1]]))