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.
We have posted a preprint Wisdom of the crowd from unsupervised dimension reduction on arXiv. In this paper we show that one-dimensional unsupervised dimension reduction, such as principal component analysis and Isomap, can be used to derive consensus predictions from the responses of multiple individuals to the same questions, and performs better than existing solutions. This is relevant for crowd wisdom applications in the social and natural sciences, including data fusion, meta-analysis, crowd-sourcing, and committee decision making.
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.
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.