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Open Access Method

Differential analysis of high-throughput quantitative genetic interaction data

Gordon J Bean1 and Trey Ideker1234*

Author Affiliations

1 Bioinformatics and Systems Biology Program, University of California, San Diego, 9500 Gilman Drive, Dept. 0419, La Jolla, CA 92093-0419, USA

2 Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0412, La Jolla, CA 92093-0412, USA

3 Institute for Genomic Medicine, University of California, San Diego, 9500 Gilman Drive, 0642, La Jolla, CA 92093, USA

4 Department of Medicine, University of California, San Diego, 9500 Gilman Drive, # 0671, La Jolla, CA 92093-0671, USA

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Genome Biology 2012, 13:R123  doi:10.1186/gb-2012-13-12-r123

Published: 26 December 2012

Abstract

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m webcite.