tool.cluster: Hierarchical clustering of nodes

In Mergeomics: Integrative network analysis of omics data

Description

tool.cluster performs agglomerative hierarchical clustering for nodes (genes)

Usage

tool.cluster(edges, cutoff = NULL)

Arguments

edges	edge (weight) list among two group, whose overlapping information (overlapping ratio based on shared entries of two groups, number of members in both group) had been assesed previously
cutoff	cutting level of dendrogram for hierarchical clustering

Details

tool.cluster takes overlapping information between two groups, produces distance matrix based on 1-strength(overlap) ratio between two groups, and apply agglomerative hierarchical clustering based on the distance matrix.

Value

res	data list including clustering results: CLUSTER: cluster label NODE: item (node) name

Author(s)

Ville-Petteri Makinen

References

Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.

Examples

## read the coexpr module file as an example:
moddata <- tool.read(system.file("extdata",
"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))

## let us cluster the first 10 modules in the module file:
mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## Find clusters.
rmax = 0.33
edges <- tool.overlap(items=moddata$GENE, groups=moddata$MODULE)
clustdat <- tool.cluster(edges, cutoff=rmax)
nclust <- length(unique(clustdat$CLUSTER))
nnodes <- length(unique(clustdat$NODE))

Mergeomics documentation built on Nov. 8, 2020, 6:58 p.m.