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Comparative Pathway Integrator: A Framework of Meta-Analytic Integration of Multiple Transcriptomic Studies for Consensual and Differential Pathway Analysis

Zeng, Xiangrui and Zong, Wei and Lin, Chien-Wei and Fang, Zhou and Ma, Tianzhou and Lewis, David A. and Enwright, John F. and Tseng, George C. (2020) Comparative Pathway Integrator: A Framework of Meta-Analytic Integration of Multiple Transcriptomic Studies for Consensual and Differential Pathway Analysis. Genes, 11 (6). p. 696. ISSN 2073-4425

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Abstract

Pathway enrichment analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. However, when multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy introduced by combining multiple public pathway databases hinders interpretation and knowledge discovery. We present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fisher’s method to discover consensual and differential enrichment patterns, a tight clustering algorithm to reduce pathway redundancy, and a text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well as novel enrichment patterns. CPI’s R package is accessible online on Github metaOmics/MetaPath.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zeng, Xiangrui
Zong, Weiwez97@pitt.edu
Lin, Chien-Wei
Fang, Zhou
Ma, Tianzhou
Lewis, David A.lewisda@upmc.edu
Enwright, John F.jfe10@pitt.edu
Tseng, George C.ctseng@pitt.edu
Date: 24 June 2020
Date Type: Publication
Journal or Publication Title: Genes
Volume: 11
Number: 6
Publisher: MDPI AG
Page Range: p. 696
DOI or Unique Handle: 10.3390/genes11060696
Schools and Programs: School of Public Health > Biostatistics
Refereed: Yes
Uncontrolled Keywords: pathway, meta-analysis, text mining
ISSN: 2073-4425
Official URL: http://dx.doi.org/10.3390/genes11060696
Funders: National Institute of Health
Article Type: Research Article
Date Deposited: 12 May 2022 18:48
Last Modified: 12 May 2022 18:48
URI: http://d-scholarship.pitt.edu/id/eprint/42974

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