deTS: tissue-specific enrichment analysis to decode tissue specificity

Author:

Pei Guangsheng1,Dai Yulin1ORCID,Zhao Zhongming123ORCID,Jia Peilin1ORCID

Affiliation:

1. School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

2. Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

3. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Abstract

Abstract Motivation Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles. Results We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies. Availability and implementation https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/ Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Institutes of Health

Cancer Prevention & Research Institute of Texas

CPRIT

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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