Analysing the meta-interaction between pathways by gene set topological impact analysis
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METHODOLOGY ARTICLE
Open Access
Analysing the meta-interaction between pathways by gene set topological impact analysis Shen Yan1†, Xu Chi2,3†, Xiao Chang4 and Mengliang Tian1*
Abstract Background: Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. Results: To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. Conclusions: GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/ module analysis results. Keywords: Topological pathway analysis, Algorithm development, Functional module
Background Pathway analysis is a routine process in transcriptome analysis used to gain biological insights. As reviewed in several recent works [1–3], such analysis can be roughly categorized into three groups: Over Representative Analysis (ORA), Functional Class Scoring (FCS), and Pathway Topology Based (PTB) analysis. The ORA methods, exemplified by GO enrichment analysis [4, 5], are based on hypergeometric distribution and the over-representation of the shared genes between pathways. The FCS method, exemplified by GSEA [6], incorporates the level of gene * Correspondence: [email protected] † Shen Yan and Xu Chi contributed equally to this work. 1 College of Agronomy, Sichuan Agricultural University, Chengdu 611130, Sichuan, China Full list of author information is available at the end of the article
expression changes in weighting the calculation of the enrichment score. Since these tools do not consider the topological structure of the gene interaction networks, more recently developed algorithms convert the topological structure of the pathways into impact scores (e.g. SPIA) [7] or weights (e.g. CePa, NetPathMiner) [8, 9] before incorporating them in the enrichment algorithm. Despite the wide application of these tools in transcriptome analysis, there are biases and false positives in these pathway analysis results. Donato et al. 2013 [10] showed that unrelated pathways may also be significantly enriched by pathway analysis due to shared genes (crosstalk) in the p
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