The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses
Open accessAbstract
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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“However, under the assumptions of an unconfounded experimental design with batches containing both controls and cases, batch effects can be removed by adding a batch covariate to the regression model71. This increases the power compared to non-batch corrected analyses72–74.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [73]
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Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
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Metadata
- 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
- Venue
- Nature Protocols
- Published
- 2012-02-16
- Volume
- 7
- Issue
- 3
- Pages
- 500-507
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- 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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