A novel signaling pathway impact analysis
Adi L. Tarca,Sorin Draghici,Purvesh Khatri,Sonia S. Hassan,Pooja Mittal,Jung-Sun Kim,Chong Jai Kim,Juan Pedro Kusanovic,Roberto Romero +8 more
TLDR
A novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition.Abstract:
Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.
Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.
Availability: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.read more
Citations
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Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
TL;DR: This work developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results that is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software and is a significant advance in the interpretation of enrichment analysis.
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Ten years of pathway analysis: current approaches and outstanding challenges.
TL;DR: The evolution of knowledge base–driven pathway analysis over its first decade is discussed, distinctly divided into three generations, and a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods are identified.
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Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap
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TL;DR: This protocol describes pathway enrichment analysis of gene lists from RNA-seq and other genomics experiments using g:Profiler, GSEA, Cytoscape and EnrichmentMap software, and describes innovative visualization techniques.
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TL;DR: UNLABELLED MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways.
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