ClusterGeneList: Generate Genes from a Cluster List

In MantelCorr: Compute Mantel Cluster Correlations

Description

'ClusterGeneList' produces a list of both significant and nonsignificant genes from each respective cluster type

Usage

ClusterGeneList(clus, clustlist.sig, x.data)

Arguments

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)

Value

A list with components:

SignificantClusterGenes	significant cluster genes returned from 'ClusterList'
NonSignificantClusterGenes	nonsignificant cluster genes returned from 'ClusterList'

Note

argument 'x.data' should have an ID gene variable, 'probes', attached as a 'dimnames' attribute

Author(s)

Brian Steinmeyer

See Also

'GetClusters' 'ClusterList'

Examples

# 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)

MantelCorr documentation built on Nov. 8, 2020, 4:58 p.m.