geneFilter: geneFilter

Description Usage Arguments Value Author(s) References Examples

Description

the function to filter genes by Intergrative Correlation

Usage

1
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

returns a list of ExpressionSets matrix or RangedSummarizedExperiment

objects with genes filtered

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

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

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set.seed(8)


library(curatedOvarianData)


library(GenomicRanges)


data(GSE17260_eset)


data(E.MTAB.386_eset)


data(GSE14764_eset)


## to save time, we take a small subset from each dataset


esets.list <- list(GSE17260=GSE17260_eset[1:50, 1:10], 


                   E.MTAB.386=E.MTAB.386_eset[1:50, 1:10], 


                   GSE14764=GSE14764_eset[1:50, 1:10])


rm(E.MTAB.386_eset, GSE14764_eset, GSE17260_eset)





result.set <- geneFilter(esets.list, 0.1)


dim(result.set[[1]])





## 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])


dim(result.oneset[[1]])








## Support matrices


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


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


})


result.set <- geneFilter(X.list, 0.1)


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]]))

simulatorZ documentation built on Nov. 1, 2018, 2:25 a.m.