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Bioinformatics. 2019 Mar 1. pii: btz138. doi: 10.1093/bioinformatics/btz138. [Epub ahead of print]

deTS: tissue-specific enrichment analysis to decode tissue specificity.

Pei G1, Dai Y1, Zhao Z1,2,3, Jia P1.

Author information

1
Center for Precision Health, School of Biomedical Informatics.
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

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 (GWAS) 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:

https://github.com/bsml320/deTS.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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