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Threshold Selection In Gene Co-Expression Networks Using Spectral Graph Theory Techniques, Andy D. Perkins, Michael A. Langston

Faculty Publications and Other Works -- EECS

Abstract

Background

Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff.

Results

We apply spectral graph theory methods to develop a systematic method for threshold selection. …

A Module-Based Analytical Strategy To Identify Novel Disease-Associated Genes Shows An Inhibitory Role For Interleukin 7 Receptor In Allergic Inflammation, Reza Mobini, Bengt A. Andersson, Jonas Erjefält, Mirjana Hahn-Zoric, Michael A. Langston, Andy D. Perkins, Lars O. Cardell, Mikael Benson

Faculty Publications and Other Works -- EECS

Background

The identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes.

Results

To test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This …