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##What is PEER## PEER is a collection of Bayesian approaches to infer hidden determinants and their effects from gene expression profiles using factor analysis methods. Applications of PEER have

• detected batch effects and experimental confounders
• increased the number of expression QTL findings by threefold
• allowed inference of intermediate cellular traits, such as transcription factor or pathway activations

The PEER model, inference, and applications are described in

• A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. (PLoS Computational Biology, May 2010)
• Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes. (PLoS Genetics, January 2011)

and several other projects have successfully used PEER.

This project offers an efficient and versatile C++ implementation of the underlying algorithms with user-friendly interfaces to R and python. To get started using PEER, download the source or binary versions, see the installation instructions, and take a look at the getting started tutorial.

##News##

• 01/08/2011 Extended examples https://github.com/downloads/PMBio/peer/peer_examples.tar.gz, showing how to use PEER on yeast eQTL datasets
• 20/07/2011 Support for sparse factor analysis with prior information added
• 05/07/2011 PEER can now also be installed as R source package (CRAN forthcoming)
• 01/07/2011 Support for probe-specific measurement uncertainty added

##Links##

• Installation instructions
• Getting started
• FAQ
• Documentation
• Projects and publications that use PEER
• Contact

##Who is behind PEER##

• Oliver Stegle
• Matias Piipari
• Leopold Parts

PEER was originally developed in research groups of John Winn at Microsoft Research, Cambridge, and Richard Durbin at the Wellcome Trust Sanger Institute.