1. Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Wei Liu et al. Bioinformatics. 2013 Sep 1;29(17):2169-77. doi: 10.1093/bioinformatics/btt373. Epub 2013 Jul 10. Bioinformatics. 2013/09/30 Yamada

2. Target of study With microarray data Assessment of clinical (cancer) conditions Motivation of study Poor/unsatisfied results with methods handling genes individually Usage of gene-pathway information has been promising, but still NOT satisfactory … Particularly POOR REPRODUCABILITY : NOT ROBUST Novelty of study Change in the way to handle gene-pathway: – From sets of genes – To sets of genes with structure of individual sets – Weigh nodes based on network topology using “Directed” random walk instead of “Undirected” random walk

3. Introduction

4. Microarray Expression profiles http://en.wikip edia.org/wiki/D NA_microarray http://en.wikip edia.org/wiki/D NA_microarray Data sets from GEO GEO

5. Gene pathway http://www.nature.com/nature/journal/v476/ n7359/fig_tab/nature10251_F3.html

6. Pathway information resources http://www.genome.jp/kegg/ http://www.geneontology.org/ a controlled vocabulary of termsa controlled vocabulary of terms for describing gene product characteristics to map elementary datasets (genes, proteins, small molecules, etc.) to network datasets

7. Network and hubs

8. Network theory/graph theory and hubs Nodes with many edges ~ high degree nodes Only few hubs Visualized with ba-model in igraphassisted by Cytoscape2.5. Degrees of nodes are indicated by size and color igraphCytoscape2.5 Scale-free graph Random graph Degree Frequency Real-world networks “http://ja.wikipedia.org/wiki/ “http://ja.wikipedia.org/wiki/ 複雑系ネット ワーク ” http://en.wikipedia.org/wiki/Scale- free_network

9. Topology Network topology http://en.wikipedia.org/wiki/Network_topology Which ones are hubs?

10. Hubs and Robustness What is Robustness? What is Robustness in expression analysis? What does it mean that hubs are robust in expression networks?

11. Random walk on Directed and Undirected Graphs

12. Construction of global-directed pathway graph SubpathwayMiner software (R package NOT MAINTAINED for the latest R version) 300 pathwas from KEGG 1 Global directed graph with 4113 node genes with 40875 edges Virtual node

13. Random Walk on a Graph n nodes are scored with t-test at time 0 (W(0)) W(t): Scores of node 1,2,…,n at time t M: Row-normalized adjacency matrix of graph r: (0,1),Restart probability to respect scores at time t

14. Directed Random Walk with R http://d.hatena.ne.jp/ryamada22/20130927

15. Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29:2169-2177 © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com Directed Random Walk Genes are RE-scored with DRW

16. Pathway activity Each gene is weighted based on DRW. Genes in the pathway Pj contributes to the activity. Genes with significant contribution are selected with FDR. Higher activity ~ Higher expression Lower activity ~ Lower expression Over-activity/Under-activity in total matters. Average expression across samples matters. Standardized for number of genes and gene weight to reflect relative weight within the pathway.

17. Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29:2169-2177 © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

18. Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29:2169-2177 © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com t-scores for pathways

19. These t-scores are the “SCORES” of this method for pathways. How robust ~ reproducible are they?

20. Reproducibility power K-fold cross-validation K-fold cross-validation In k-fold cross-validation, the original sample is randomly partitioned into k equal size subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. Validation sets Test sets No. pathways Kind of inner product of two vectors, test set vector and validation set vector.

21. Classification of samples using highly- scored pathways Using more reproducible ~ reliable scoring method, classification would be more reliable… This part is not today’s target.