A preprint on “Model-based clustering of multi-tissue gene expression data” is available from arXiv. In this paper we present a Bayesian model-based clustering algorithm for large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. Our model can incorporate prior information on physiological tissue similarity, and results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. The algorithm has been implemented in the Lemon-Tree software as a new “task”, revamp.
Whole-transcriptome causal network inference with genomic and transcriptomic data (on bioRxiv) describes a protocol for reconstructing causal gene networks from genome-wide genotype and gene expression data using the Findr software.
Learning differential module networks across multiple experimental conditions (on arXiv) reviews the theory of module network inference and describes how differential module networks across multiple experimental conditions can be learned using the Lemon-Tree software.
New preprint posted: Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. Related software here.
We have posted a preprint “Efficient causal inference with hidden confounders from genome-transcriptome variation data”. In this paper we introduce a new method for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors. The method has been implemented in the Findr software, and validated using the DREAM5 Systems Genetics Challenge and GEUVADIS datasets.
We published a paper describing the Lemon-Tree software in the PLOS Computational Biology Software article collection:
Bonnet E, Calzone L, Michoel T. (2015) Integrative multi-omics module network inference with Lemon-Tree. PLoS Comput Biol 11(2): e1003983.
We posted a preprint titled “Integrative multi-omics module network inference with Lemon-Tree” on the arXiv. The preprint describes the current status of our module networks inference software Lemon-Tree and demonstrates how it can be used to identify cancer driver genes from large-scale copy number variation and gene expression datasets such as generated by The Cancer Genome Atlas. All of this is joint work with Eric Bonnet.
Source code and test data accompanying our paper Natural coordinate descent algorithm for L1-penalised regression in generalised linear models are now available from Google code.
A preprint of our paper kruX: Matrix-based non-parametric eQTL discovery is available from the arXiv. This paper describes a software tool called kruX for performing millions of non-parametric ANOVA (Kruskal-Wallis) tests at once using matrix-multiplication methods. kruX is about 3 orders of magnitude faster than performing these tests one-by-one, which makes a difference if you want to do billions of them!