tool.cluster {Mergeomics} | R Documentation |
tool.cluster
performs agglomerative hierarchical clustering for
nodes (genes)
tool.cluster(edges, cutoff = NULL)
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 |
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.
res |
data list including clustering results: CLUSTER: cluster label NODE: item (node) name |
Ville-Petteri Makinen
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.
## 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))