New preprint posted: Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. Related software here.
Lingfei’s paper “Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation” has been published in PLOS Computational Biology. Congrats Lingfei!
Lingfei has posted a preprint Comparable variable selection with Lasso on arXiv. In this paper we propose statistical tests to evaluate the quality of a set of p-values and to compare p-values across different experimental batches. We then use these tests to show that a newly proposed lasso-based variable selection statistic allows for a unified FDR control across multiple variable selection tasks, unlike existing methods.
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.
Update: Cell Systems has published an Editorial Preview of our paper.
Husain’s paper “Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease” has been published in Cell Systems, the new flagship systems biology journal from Cell Press. In this paper we reconstructed and analyzed regulatory gene networks across 7 vascular and metabolic tissues using genotype and gene expression data sampled from more than 100 individuals. Here’s the graphical abstract:
Chris’ paper Functional transcription factor target discovery via compendia of binding and expression profiles has been published in Scientific Reports. Congrats Chris!
A preprint of Chris’s paper on “Functional transcription factor target discovery via compendia of binding and expression profiles” is available from the arXiv. In this paper we demonstrated that prediction of functional target genes responding to the silencing of a transcription factor (TF) can be improved by correlating a gene’s TF-binding and expression profiles across multiple experimental conditions.
A preprint written by Lingfei on “Detection of regulator genes and eQTLs in gene networks” is available from the arXiv. This is a review article/book chapter reviewing the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.
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.