Unsupervised gene set testing based on random matrix theory

BMC Bioinformatics

Methodology Article

H. Robert Frost¹  Christopher I. Amos¹ 

[1] Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, 03755, Hanover, NH, USA;

关键词: Gene set testing;    Pathway analysis;    Random matrix theory;    Tracy-Widom;    Marc̆enko-Pastur;   

DOI  :  10.1186/s12859-016-1299-8

 received in 2016-09-02, accepted in 2016-10-21,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGene set testing, or pathway analysis, is a bioinformatics technique that performs statistical testing on biologically meaningful sets of genomic variables. Although originally developed for supervised analyses, i.e., to test the association between gene sets and an outcome variable, gene set testing also has important unsupervised applications, e.g., p-value weighting. For unsupervised testing, however, few effective gene set testing methods are available with support especially poor for several biologically relevant use cases.ResultsIn this paper, we describe two new unsupervised gene set testing methods based on random matrix theory, the Marc̆enko-Pastur Distribution Test (MPDT) and the Tracy-Widom Test (TWT), that support both self-contained and competitive null hypotheses. For the self-contained case, we contrast our proposed tests with the classic multivariate test based on a modified likelihood ratio criterion. For the competitive case, we compare the new tests against a competitive version of the classic test and our recently developed Spectral Gene Set Enrichment (SGSE) method. Evaluation of the TWT and MPDT methods is based on both simulation studies and a weighted p-value analysis of two real gene expression data sets using gene sets drawn from MSigDB collections.ConclusionsThe MPDT and TWT methods are novel and effective tools for unsupervised gene set analysis with superior statistical performance relative to existing techniques and the ability to generate biologically important results on real genomic data sets.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

12864_2017_3669_Article_IEq1.gif

12864_2017_4186_Article_IEq16.gif

12864_2017_3670_Article_IEq7.gif

12864_2017_3670_Article_IEq5.gif

12864_2017_3790_Article_IEq1.gif

12864_2017_3938_Article_IEq12.gif

12864_2017_3938_Article_IEq11.gif

12864_2017_3938_Article_IEq10.gif

12864_2017_3938_Article_IEq8.gif

12864_2017_3938_Article_IEq7.gif

12645_2016_25_Article_IEq19.gif

12864_2017_3487_Article_IEq8.gif

12864_2017_4191_Article_IEq1.gif

12864_2017_3938_Article_IEq3.gif

12864_2017_3496_Article_IEq2.gif

12864_2017_3938_Article_IEq1.gif

12864_2015_2055_Article_IEq79.gif

12864_2016_3440_Article_IEq34.gif

12864_2015_2296_Article_IEq159.gif

12864_2016_3440_Article_IEq32.gif

12864_2015_2296_Article_IEq99.gif

12864_2017_4025_Article_IEq9.gif

12864_2015_2198_Article_IEq46.gif

12864_2017_4025_Article_IEq7.gif

12864_2017_4025_Article_IEq5.gif

12864_2017_4025_Article_IEq4.gif

12864_2017_4030_Article_IEq1.gif

12864_2017_4025_Article_IEq2.gif

12864_2016_2443_Article_IEq13.gif

12864_2015_2118_Article_IEq5.gif

12864_2017_3785_Article_IEq2.gif

12864_2016_2789_Article_IEq52.gif

12711_2017_284_Article_IEq69.gif

12864_2017_3604_Article_IEq1.gif

12864_2017_4309_Article_IEq11.gif

12864_2015_2055_Article_IEq59.gif

12711_2017_365_Article_IEq126.gif

12864_2017_3821_Article_IEq5.gif

12864_2017_4225_Article_IEq2.gif

12864_2017_4020_Article_IEq29.gif

12864_2017_4020_Article_IEq27.gif

12864_2017_3821_Article_IEq2.gif

12864_2015_2296_Article_IEq87.gif

12864_2017_3655_Article_IEq15.gif

12864_2017_3742_Article_IEq1.gif

12864_2015_2296_Article_IEq86.gif

12864_2016_3440_Article_IEq16.gif

12864_2017_3500_Article_IEq3.gif

12864_2017_3733_Article_IEq71.gif

12864_2017_3733_Article_IEq69.gif

12864_2017_4227_Article_IEq1.gif

12864_2017_3492_Article_IEq22.gif

12864_2017_3492_Article_IEq20.gif

12864_2017_4269_Article_IEq3.gif

12864_2017_3492_Article_IEq18.gif

12864_2017_3605_Article_IEq20.gif

12864_2017_3605_Article_IEq19.gif