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

This project offers an efficient and versatile C++ implementation of the underlying algorithms with user-friendly interfaces to R and python.

[Genome Research Limited]

Overview

Gene expression level is a complex phenotype. Its measurement in an experiment can be affected by a wide range of factors - state of the cell, experimental conditions, variants in the sequence of regulatory regions, etc. To understand genotype to phenotype relationships, we need to be able to distinguish the variation that is due to genetic state from all the confounding causes.

Here, we present PEER, a probabilistic framework for understanding sources of variation in high-dimensional phenotype data. PEER allows models for different sources of variation in the observed phenotype to be quickly and flexibly combined, resulting in more accurate estimates of effect of variable of interest.

Download

C++ source with Python and R bindings and standalone tools: Source

Publications

• The architecture of gene regulatory variation across multiple human tissues: the MuTHER study.

  Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman AK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang TP, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, Ahmadi K, Deloukas P, McCarthy MI, Dermitzakis ET, Spector TD and MuTHER Consortium

  PLoS genetics 2011;7;2;e1002003

  PUBMED: 21304890; PMC: 3033383; DOI: 10.1371/journal.pgen.1002003

• Joint genetic analysis of gene expression data with inferred cellular phenotypes.

  Parts L, Stegle O, Winn J and Durbin R

  PLoS genetics 2011;7;1;e1001276

  PUBMED: 21283789; PMC: 3024309; DOI: 10.1371/journal.pgen.1001276

• A map of human genome variation from population-scale sequencing.

  1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME and McVean GA

  Nature 2010;467;7319;1061-73

  PUBMED: 20981092; PMC: 3042601; DOI: 10.1038/nature09534

• A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

  Stegle O, Parts L, Durbin R and Winn J

  PLoS computational biology 2010;6;5;e1000770

  PUBMED: 20463871; PMC: 2865505; DOI: 10.1371/journal.pcbi.1000770

• Accounting for Non-genetic Factors Improves the Power of eQTL Studies

  Stegle O, Kannan A, Durbin R and Winn J

  Lecture Notes in Computer Science (RECOMB) 2008

  DOI: 10.1007/978-3-540-78839-3_35

Documentation

PEER usage

PEER tutorial

Email lp2@sanger.ac.uk with any issues with running PEER.