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 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0.6 0.9.9.6.9.8.6.3.4.6.5.3.2.3.1 Input Gene Set Pathway 1 Pathway N ……. RWR.6.5.4.3.2.1 ……. Pathway 1 Pathway N

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 1.select 100 most DEGs 2.get association scores of EnrichNet and ORA 3.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