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EnrichNet : network-based gene set enrichment analysis

EnrichNet : network-based gene set enrichment analysis. Presenter: Lu Liu. The problem: Functional Interpretation. Identify and assess functional associations between an experimentally derived gene/protein set and well-known gene/protein sets. Agenda. Related research The method

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EnrichNet : network-based gene set enrichment analysis

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  1. EnrichNet: network-based gene set enrichment analysis Presenter: Lu Liu

  2. The problem: Functional Interpretation • Identify and assess functional associations between an experimentally derived gene/protein set and well-known gene/protein sets

  3. Agenda • Related research • The method • The Evaluation • The results • The conclusion

  4. Related Research • Over-representation analysis (ORA) • Gene set enrichment analysis (GSEA) • Modular enrichment analysis (MEA)

  5. Limitations • ORA tend to have low discriminative power • Functional information from interaction network disregarded • Missing annotation gene/protein ignored • Tissue-specific gene/protein set association often infeasible

  6. Agenda • Related research • The method • The Evaluation • The results • The conclusion

  7. General workflow • Input gene/protein list(>=10), a database of interest (KEGG etc.) • Processing gene mapping, score the distance with RWR, compare scores with background model • Output A pathways/processes ranking table, visualization of sub-networks

  8. Input

  9. Output

  10. The method • .3 0 Pathway 1 • .5 0 Pathway 1 • .3 0 0 0 • .6 • .6 .6 .6 .6 .6 .5 Input Gene Set 0 • .6 • .6 0 • .9 0.9 1 ……. • .6 0 RWR ……. 1 • .9 • .6 • .6 0 0 • .3 0 1 • .9 • .6 0 • .4 0 .4 .3 .2 .1 • .1 0 1 • .8 • .3 0 Pathway N • .3 0 Pathway N • .2 0 • .4 0

  11. Algorithm for distance score

  12. Relate scores to a background model • Discretized into equal-sized bins • Quatify each pathway’s deviation from average

  13. Agenda • Related research • The method • The Evaluation • The results • The conclusion

  14. Evaluation method • Compare with ORA 5 datasets and 2 reference gene sets from literature • select 100 most DEGs • get association scores of EnrichNet and ORA • compute a running-sum statistic for all gene sets • The consensus of GSEA-derived(SAM-GS, GAGE) pathway ranking as external benchmark pathway ranking

  15. Agenda • Related research • The method • The Evaluation • The results • The conclusion

  16. The results-EnrichNet vs ORA

  17. The results-Xd-score vs Q-value

  18. The results-comparative validation

  19. Protein–protein interaction sub-networks (largest connected components) for target and reference set pairs with small overlap, predicted to be functionally associated by EnrichNet: (a) gastric cancer mutated genes (blue) and genes/proteins from the BioCarta pathway ‘Role of Erk5 in Neuronal Survival’ (magenta, the shared genes are shown in green); (b) bladder cancer mutated genes (blue) and genes/proteins from Gene Ontology term ‘Tyrosine phosphorylation of Stat3’ (GO:0042503, magenta; the only shared gene NF2 is shown in green).

  20. Protein–protein interaction sub-network (largest connected component) for the PD gene set (blue) and genes/proteins from GO term ‘Regulation of interleukin-6 biosynthetic process’ (magenta, GO:0045408; the only shared gene IL1B is shown in green).

  21. The results-tissue specificity • EnrichSet don’t require additional gene expression measurement data • Brain tissue: Xd-scores over-representated • Non-Brain tissue: center of Xd-score distribution significant lower

  22. The conclusion • EnrichNet sometimes has more discriminative power when target sets and pathway set has large overlaps • EnrichNet can identifies novel function associations through direct and indirect molecular interactions when target sets and pathway set has little overlaps

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