PEER & PANAMA: estimating hidden confounders in gene expression

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

PEER is available as a command line tool, as well as python and R interfaces, which can be downloaded here.
PANAMA is a recent alternative to PEER, which provides similar functionality but can improve the results in certain settings (see [4]). A python implementation co-developed with colleges in Sheffield is available here.

References:

  1. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses
    Stegle, O.Parts L.Piipari M.Winn J., and Durbin R. Nature protocols. Volume 7, Number 3, (2012), p.500–507
  2. Joint genetic analysis of gene expression data with inferred cellular phenotypes
    Parts, L.Stegle O.Winn J., and Durbin R. PLoS genetics. Volume 7, Number 1, (2011), p.e1001276
  3. 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. Volume 6, Number 5, (2010), p.e1000770
  4. Fusi, N.Stegle O., and Lawrence N. D. PLoS computational biology. Volume 8, Number 1, (2012), p.e1002330

Gaussian Process Two Sample test

GPTwoSample is a Gaussian process based two sample test for time series datasets. 
The code release is python-based and can be downloaded online.

References:

  1. Stegle, O.Denby K. J.Cooke E. J.Wild D. L.Ghahramani Z., and Borgwardt K. M. Journal of Computational Biology. Volume 17, Number 3, (2010), p.355–367