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

By Oliver Stegle, Leopold Parts, Matias Piipari, John M. Winn, Richard Durbin. 2012

958 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Anchor regression: Heterogeneous data meet causality (2018, background): “The genotyping principal components and PEER factors (which are constructed from covariates and gene expressions) account for some (but not all) of the confounding sources of expression variation, such as batch effects, environmental influences and sample history [Stegle et al., 2012].”
Rewiring of the Fruit Metabolome in Tomato Breeding. (2018, methodology): “…Langfelder and Horvath, 2008 https://labs.genetics.ucla.edu/horvath/htdocs/ CoexpressionNetwork/Rpackages/WGCNA Matrix eQTL Shabalin, 2012 http://bios.unc.edu/research/genomic_ software/Matrix_eQTL PEER Stegle et al., 2012 https://github.com/PMBio/peer”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “Latent-variable models, which are built on approaches applied in bulk RNA-seq studies to infer and correct for hidden factors that cause gene expression heterogeneity 56,57,59 , can be used to deduce the correlation between cells due to factors such as the cell cycle or apoptotic state.”
Genomic basis for RNA alterations in cancer (2020, methodology): “Moreover, we looked at the overlap between somatic eQTL and 72,987 GeneHancer 89 enhancers-to-genes interactions, with at least two independent supporting methods (called ‘double-elite’), downloaded from the UCSC hg19 GeneHancer track 90 .”
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells (2016, background): “4.14 N/A http://bedops.readthedocs.io/en/latest/ ComBat Chen et al., 2011 http://www.bioconductor.org/packages/release/bioc/html/ sva.html PEER Stegle et al., 2012 https://github.com/PMBio/peer/wiki DNA Methylation Age Calculator Horvath, 2013 https://dnamage.genetics.ucla.edu/ LIMIX Casale et al.,…”
NOREVA: normalization and evaluation of MS-based metabolomics data (2017, methodology): “Secondly, data pre-processing corrected signals by QC-RLSC (5) and missing value imputation (56).”
Computational principles and challenges in single-cell data integration (2021, methodology): “Similarly to eQTL analyses, where tools such as PCA and PEER are used to guard against this, such cell-centric attributes can be added as covariates in a linear regression framework.”
RNA Sequencing and Analysis (2015, positive): “For example, the probabilistic estimation of expression residuals (PEER) framework (Stegle et al. 2012) and the hidden covariates with prior (HCP) framework (Mostafavi et al. 2013) are methods that use a Bayesian approach to infer hidden covariates and remove their effects from expression data.”
Computational solutions for omics data (2013, methodology): “The sparse factor analysis method PEER 95,96 has been developed to infer a small set of ‘hidden cellular phenotypes’ or expression patterns in the gene expression data that can explain the highest variability in gene expression across multiple samples.”