Differential analysis of high-throughput quantitative genetic interaction data

被引：17

作者：

Bean, Gordon J. ^{[1
]}

Ideker, Trey ^{[1
,2
,3
,4
]}

机构：

[1] Univ Calif San Diego, Bioinformat & Syst Biol Program, La Jolla, CA 92093 USA

[2] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA

[3] Univ Calif San Diego, Inst Genom Med, La Jolla, CA 92093 USA

[4] Univ Calif San Diego, Dept Med, La Jolla, CA 92093 USA

来源：

GENOME BIOLOGY | 2012年 / 13卷 / 12期

基金：

美国国家卫生研究院;

关键词：

STATISTICAL TESTS; DNA-DAMAGE; FUNCTIONAL-ORGANIZATION; YEAST; TRANSCRIPTION; EXPRESSION; NETWORKS;

D O I：

10.1186/gb-2012-13-12-r123

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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/similar to gbean/compute_differential_scores.m.

引用

页数：10

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