News

Wisdom of the crowd preprint

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.

GRN book chapters

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.

Lasso p-value preprint

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.

Findr preprint

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.