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Meta-Analysis
. 2020 Jun 24;11(6):696.
doi: 10.3390/genes11060696.

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

Xiangrui Zeng  1 , Wei Zong  2 , Chien-Wei Lin  3 , Zhou Fang  2 , Tianzhou Ma  4 , David A Lewis  5 , John F Enwright  5 , George C Tseng  2
Affiliations
Meta-Analysis

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

Xiangrui Zeng et al. Genes (Basel). .

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.

Keywords: meta-analysis; pathway; text mining.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow of Comparative Pathway Integrator (CPI).
Figure 2
Figure 2
Workflow of noun phrase extraction.
Figure 3
Figure 3
Heatmap of kappa statistics of pair-wise pathways in all clusters.
Figure 4
Figure 4
Heatmap of log10-scale pathway enrichment p-values of pathways annotated by eight pathway clusters (I-VIII) and a scattered pathway set (black).
Figure 5
Figure 5
Hierarchical clustering of psychiatric studies in each cluster with distance defined by the log10-scale pathway enrichment p-values.
See this image and copyright information in PMC

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