Abstract
AbstractThe regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R (≥ 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).
Publisher
Cold Spring Harbor Laboratory
Reference31 articles.
1. The Cancer Genome Atlas, https://cancergenome.nih.gov/.
2. International Cancer Genome Consortium, http://icgc.org/.
3. GWAS Catalog, https://www.ebi.ac.uk/gwas/.
4. Genome-wide identification of post-translational modulators of transcription factor activity in human B cells
5. Discovering modulators of gene expression
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献