School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Bioinformatics. 2019 Oct 1;35(19):3842-3845. doi: 10.1093/bioinformatics/btz138.
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.
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.
https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/.
Supplementary data are available at Bioinformatics online.
疾病和特征受到动态组织特异性调控。然而，生物医学研究中通常采集异质组织，这降低了鉴定与疾病相关变异和基因表达谱的能力。
我们提出了 deTS，这是一个 R 包，可使用两个内置参考面板进行组织特异性富集分析。为检测组织特异性基因和不同形式查询数据的富集检验，开发并实现了统计方法。使用多性状全基因组关联研究数据和癌症表达数据的应用表明，deTS 可以有效地识别每个查询性状或样本最相关的组织，为未来的研究提供了思路。
https://github.com/bsml320/deTS 和 CRAN https://cran.r-project.org/web/packages/deTS/。
补充数据可在《生物信息学》在线获得。
