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Network Enrichment Analysis: Method, Software, And Web-Site For Functional Interpretation Of 'Omics' Data In Global Networks Andrey Alexeyenko User-friendly implementation on-line https://www.evinet.org/ E-mail: [email protected] Telefon: +46 8 52481513 http://research.scilifelab.se/andrej_alexeyenko/ http://ki.se http://bils.se Three inputs to NEA Functional gene set e.g. pathway Network enrichment analysis Experimentally defined (altered) genes 0 50 100 150 200 250 300 350 400 450 No. of positives, random groups 0 200 400 600 800 1000 1200 1400 No. of positives, r eal groups GEA_SIGN GEA_REST NEA_SIGN NEA_REST Gene set enrichment analysis Alexeyenko A., Lee W., … Pawitan Y. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics, 2012 1) Your gene/protein set: mutations, rare variants, candidate disease genes, differentially expressed genes. 2) A collection of previously known functional sets: pathways, Gene Ontology terms etc. 3) A global network of interactions and relations between genes and proteins (known and/or predicted) Comparison to state-of-the-art method P53 signaling Cell cycle Apoptosis 0 2 4 6 8 0.0 0.2 0.4 0.6 0.8 1.0 Years since surgery Relapse-free survival Predictors: GO:0001666 response to hypoxia GO:0005154 EGFR binding GO:0005164 TNF binding GO:0070848 response to growth factor stimulus Example applications a) Distinguishing between driver and passenger cancer mutations Red: drivers Black: passengers b) Identifying driver copy number changes in cancer confidence copy number distribution in the cohort c) Network enrichment scores as predictors of clinical features When used as biomarkers, the scores are often superior to gene expression and mutation profiles Network enrichment analysis: the concept The overall goal of our tools for network enrichment analysis (NEA) is functional exploration of experimental data in either hypothesis-driven or hypothesis-free manner. Similarly to any biological observation, statistical significance of network patterns should always be estimated. This is done by comparing the network connectivity score either between or within gene sets to that expected by chance. The method extends the gene set enrichment analysis (GSEA) into the network domain. The output and interpretation of NEA are similar to those of GSEA, but the sensitivity of NEA is higher ~5-10-fold. In addition, NEA is applicable to both gene sets and single genes, including those lacking any GO and pathway annotations. NEA can also accommodate any custom/proprietary network Alexeyenko A. and Sonnhammer E. Global networks of functional coupling in eukaryotes by comprehensive data integration Genome Research, 2009
