Reconciling differential gene expression data with molecular interaction networks

January 28, 2015

Reconciling differential gene expression
w/…#networkshttp://bioinformatics.oxfordjournals.org/content/29/5/622 Propagating this across interactions finds perturbed pathways

This paper basically propagates scores of disease-related highly differentially expressed genes (-log10 p) over human protein interaction network, calculates new scores using four major algorithms (Vanilla, PageRank, GeneMANIA, Heat Kernel), re-ranks genes based on the new scores and then finds enriched pathways among top-ranking genes. Compared with traditional ways by ranking highly differentially expressed genes based on p-values without any network information, the approach not only recovered canonical pathways but also discovered novel ones such as an insulin-mediated glucose transport pathway in Huntington’s disease. The authors also explored differences among four algorithms and identified the top-ranking genes specifically found by particular algorithms. In short, the paper provides a valuable framework for integrating networks and gene expression data. Their analysis for comparing four major algorithms is also helpful.

http://bioinformatics.oxfordjournals.org/content/29/5/622