title: Differential analysis of high-throughput quantitative genetic interaction data.
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.
authors.forename: Gordon J
authors.initials: GJ
authors.lastname: Bean
authors.forename: Trey
authors.initials: T
authors.lastname: Ideker
mesh_terms: D003627:Data Interpretation, Statistical; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks; D005821:Genetic Techniques; D008957:Models, Genetic; D009154:Mutation; D015003:Yeasts / Q000235:genetics