Simplify regulator-target gene network via clustering.

Description

Several regulators (specifically TFs) might have extremely overlapping target genes. In order to identify clusters of highly similar regulators (mainly TFs) we implemented a network simplification algorithm in biRte: We construct the biadjacency matrix of the complete bipartite regulator target-gene graph and then calculate a single linkage clustering of regulators based on the Tanimoto-Jaccard similarity of their target genes. The dendrogram is cut at a defined height (default: 0.1) to idenfity resulting groups. The algorithm is meant to simplify the inference of active regulators, because the resulting regulator clusters have more dissimilar target gene profiles.

Usage

simplify(affinities, cutoff=0.9)

Arguments

Value

clustered / simplified network

Author(s)

Holger Froehlich

Examples

# artificial data
data(humanNetworkSimul)
affinities2 = simplify(affinities2)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(birte)
Loading required package: RcppArmadillo
Loading required package: Rcpp
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/birte/simplify.Rd_%03d_medium.png", width=480, height=480)
> ### Name: simplify
> ### Title: Simplify regulator-target gene network via clustering.
> ### Aliases: simplify
> 
> ### ** Examples
> 
> # artificial data
> data(humanNetworkSimul)
> affinities2 = simplify(affinities2)
merging 111 regulators into 111 clusters
merging 13 regulators into 13 clusters
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>