We have contributed two chapters to a forthcoming book on gene regulatory network inference edited by Vân Anh Huynh-Thu and Guido Sanguinetti.
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.
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.
Our software packages that were previously hosted on Google Code have all been moved to GitHub, due to Google Code’s closure.
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.
Our paper about the software tool kruX for performing millions of non-parametric ANOVA (Kruskal-Wallis) tests at once using matrix-multiplication methods has been published by BMC Bioinformatics. The final version is also available from the arXiv.
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!