Single-cell latent variable model

The single-cell latent variable model (scLVM) is an approach to reconstruct and account for hidden sources of variation in single-cell RNA_Seq studies. 

scLVM is available as python module with interfaces to R. For download and further information please see our github page.

References:

  1. Buttner, F. et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotech 33, 155-160.

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]). PANAMA can be downloaded here.

References:

  1. Stegle, O., et al. (2012) Using Probabilistic Estimation of Expression Residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat Protoc 7, 500-507
  2. Parts, L., Stegle, O., Winn, J., Durbin, R. (2011)  Joint genetic analysis of gene expression data with inferred cellular phenotypes PLoS Genet 7, 1 p.e1001276.
  3. Stegle, O., Parts, L., Durbin, R., Winn, J. (2010) A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol 6, 5 p.e1000770
  4. Fusi, N., Stegle, O., Lawrence, N.D. (2012) Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies, 8, 1 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., Borgwardt, K.M. (2010), A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. Journal of Comput Biol, 17, 3 p.355–367