Cited by 1,049Genetic Associations and EpidemiologyGene expression and cancer classificationBioinformatics and Genomic Networks

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

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Stegle, Oliver, Parts, Leopold, Piipari, Matias, Winn, John, Durbin, Richard·2012·Nature Protocols

DOI 10.1038/nprot.2011.457PMC PMC3398141

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Abstract

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.

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Authors: Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R.
Affiliations: Max Planck Institute for Intelligent Systems (DE)
Wellcome Sanger Institute (GB)
Microsoft Research (United Kingdom) (GB)
Corresponding author: Stegle, Oliver
ORCID: Stegle, Oliver
Parts, Leopold
Piipari, Matias
Durbin, Richard
Venue: Nature Protocols
Publisher: Springer Science and Business Media LLC
Published: 2012-02-16
Volume: 7
Issue: 3
Pages: 500-507
ISSN: 1750-2799 (ISSN-L)
1754-2189
DOI: 10.1038/nprot.2011.457
PMC: PMC3398141
OpenAlex: W1986911601
Cited by: 1,049
References: 39
Citations by year: 2026: 84 · 2025: 93 · 2024: 153 · 2023: 104 · 2022: 131 · 2021: 102 · 2020: 83 · 2019: 81 · 2018: 64 · 2017: 53 · 2016: 44 · 2015: 32 · 2014: 12 · 2013: 9 · 2012: 3
FWCI: 10.76
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Protocols (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Max Planck Digital Library (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion)
Funders: Wellcome Trust (WT077192 ⁄ Z ⁄ 05 ⁄ Z, 098051)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: peers, gene expression, genetic association, factor analysis, hidden factors, pathway activation, transcription factor activation, expression variability, statistical power
MeSH terms: Algorithms, Factor Analysis, Statistical, Sensitivity and Specificity, Software, Models, Statistical, Gene Expression Profiling, Genetic Association Studies

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

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