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Motivation Pathway 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. When multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy issue introduced by combining public pathway databases hinders knowledge discovery. Methods and Results 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, consensus clustering to reduce pathway redundancy, and a novel 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 novel enrichment patterns. Availability CPI is accessible online: http://tsenglab.biostat.pitt.edu/software.htm. Contact xiangruz@andrew.cmu.edu
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Comparative Pathway Integrator: a framework of meta-analytic integration of multiple transcriptomic studies for consensual and differential pathway analysis
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