ClusterGeneList {MantelCorr} | R Documentation |
'ClusterGeneList' produces a list of both significant and nonsignificant genes from each respective cluster type
ClusterGeneList(clus, clustlist.sig, x.data)
clus |
'clusters' object returned by 'GetClusters' |
clustlist.sig |
'SignificantClusters' object returned by 'ClusterList' |
x.data |
original (p x n) numeric data matrix (e.g., gene-expression data) |
A list with components:
SignificantClusterGenes |
significant cluster genes returned from 'ClusterList' |
NonSignificantClusterGenes |
nonsignificant cluster genes returned from 'ClusterList' |
argument 'x.data' should have an ID gene variable, 'probes', attached as a 'dimnames' attribute
Brian Steinmeyer
'GetClusters' 'ClusterList'
# simulate a p x n microarray expression dataset, where p = genes and n = samples data.sep <- rbind(matrix(rnorm(1000), ncol=50), matrix(rnorm(1000, mean=5), ncol=50)) noise <- matrix(runif(40000), ncol=1000) data <- t(cbind(data.sep, noise)) data <- data[1:200, ] # data has p = 1,050 genes and n = 40 samples clusters.result <- GetClusters(data, 100, 100) dist.matrices <- DistMatrices(data, clusters.result$clusters) mantel.corrs <- MantelCorrs(dist.matrices$Dfull, dist.matrices$Dsubsets) permutation.result <- PermutationTest(dist.matrices$Dfull, dist.matrices$Dsubsets, 100, 40, 0.05) # generate both significant and non-significant gene clusters cluster.list <- ClusterList(permutation.result, clusters.result$cluster.sizes, mantel.corrs) # significant and non-significant cluster genes (expression values) cluster.genes <- ClusterGeneList(clusters.result$clusters, cluster.list$SignificantClusters, data)