Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

• Published in: Nature protocols Vol. 7; no. 3; pp. 500 - 507
• Main Authors: Stegle, Oliver, Parts, Leopold, Piipari, Matias, Winn, John, Durbin, Richard
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.03.2012; Nature Publishing Group
• Subjects: 631/114/794; 631/1647/2017; Algorithms; Analysis; Analytical Chemistry; Association analysis; Biological Techniques; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer programs; Computers; Data processing; Factor analysis; Factor Analysis, Statistical; Gene expression; Gene Expression Profiling - methods; Gene Expression Profiling - statistics & numerical data; Gene mapping; Genetic aspects; Genetic Association Studies - methods; Life Sciences; Mathematical models; Microarrays; Models, Statistical; Organic Chemistry; Physiological aspects; Protocol; Sensitivity and Specificity; Software; Statistical analysis; Statistical models; Transcription; Transcription factors; Variables; Yeast fungi; United Kingdom > UK; Germany
• ISSN: 1754-2189; 1750-2799; 1750-2799
• DOI: 10.1038/nprot.2011.457

We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.