Dream: powerful differential expression analysis for repeated measures designs

被引：131

作者：

Hoffman, Gabriel E. ^{[1
,2
,3
]}

Roussos, Panos ^{[1
,2
,3
,4
,5
]}

机构：

[1] Icahn Sch Med Mt Sinai, Pamela Sklar Div Psychiat Genom, New York, NY 10029 USA

[2] Icahn Sch Med Mt Sinai, Icahn Inst Data Sci & Genom Technol, New York, NY 10029 USA

[3] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA

[4] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY 10029 USA

[5] James J Peters VA Med Ctr, Mental Illness Res Educ & Clin Ctr VISN 2 South, Bronx, NY 10468 USA

来源：

BIOINFORMATICS | 2021年 / 37卷 / 02期

关键词：

RNA-SEQ DATA; QUANTIFICATION; MODEL; METAANALYSIS; NETWORKS; RISK; LOCI;

D O I：

10.1093/bioinformatics/btaa687

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

A Summary: Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings.

引用

页码：192 / 201

页数：10

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