simplify

affinities

cut dendrogram at height 1 - cutoff (i.e. similarity cutoff)

cutoff


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.


Expression levels of mRNA molecules are regulated by
different processes, comprising inhibition or activation by
transcription factors and post-transcriptional degradation by
microRNAs. biRte uses regulatory networks of TFs, miRNAs and
possibly other factors, together with mRNA, miRNA and other
available expression data to predict the relative influence of
a regulator on the expression of its target genes. Inference is
done in a Bayesian modeling framework using
Markov-Chain-Monte-Carlo. A special feature is the possibility
for follow-up network reverse engineering between active
regulators.

Holger Froehlich

birte

Bayesian Inference of Regulatory Influence on Expression (biRte)

simplify function

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.

simplify: Simplify regulator-target gene network via clustering.

Description

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Examples