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