tool.cluster {Mergeomics} | R Documentation

## Hierarchical clustering of nodes

### 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))

[Package Mergeomics version 1.18.0 Index]