VL Network Analysis ( ) SS2016 Week 3

Transcription

1 VL Network Analysis ( ) SS2016 Week 3 Based on slides by J Ruan (U Texas) Tim Conrad AG Medical Bioinformatics Institut für Mathematik & Informatik, Freie Universität Berlin 1

2 Motivation 2

3 Lecture outline (More) basic terminology and concepts in networks Some interesting results between network properties and biological functions Network clustering / community discovery Applications of network clustering methods 3

4 Connectivity vs essentiality % of essential proteins Number of connections Jeong et. al. Nature

5 Community role vs essentiality Effect of a perturbation cannot depend on the node s degree only! Many hub genes are not essential Some non-hub genes are essential Maybe a gene s role in its community is also important Local leader? Global leader? Ambassador Nature,

6 Community structure 6

7 Role 1, 2, 3: non-hubs with increasing participation indices Role 5, 6: hubs with increasing participation indices 7

8 Dynamically organized modularity in the yeast PPI network Protein interaction networks are static Two proteins cannot interact if one is not expressed We should look at the gene expression level Han, et. al, Nature 430,

9 Obtaining Data 9

10 Distinguish party hubs from date hubs Red curve hubs Cyan curve nonhubs Black curve randomized Partners of date hubs are significantly more diverse in spatial distribution than partners of party hubs 10

11 Effect of removal of nodes on average geodesic distance Original Network On removal of date hubs Green nonhub nodes Brown hubs Red date hubs Blue party hubs The breakdown point is the threshold after which the main component of the network starts disintegrating. On removal of party hubs 11

12 Dynamically organized modularity Red circles Date hubs Blue squares - Modules 12

13 Han-Yu Chuang, Eunjung Lee, Yu-Tseung Liu, Doheon Lee, Trey Ideker, Network-based classification of breast cancer metastasis, Mol Syst Biol. 2007; 3:

14 Challenge: Predict Metastasis If metastasis is likely => aggressive adjuvant therapy How to decide the likelihood? Traditional predictive factors are not good 14

15 Recently: Gene Marker Sets Examine genome-wide expression profiles Score individual genes for how well they discriminate between different classes of disease Establish gene expression signature Problem: # genes >> # patients 15

16 Pathway Expression vs. PPI Subnetwork as Marker Score known pathways for coherence of gene expression changes? Majority of human genes not yet assigned to a definitive pathway Large Protein-Protein Interaction networks recently became available Extract subnetworks from PPI networks as markers 16

17 Subnetwork Marker Identification: Data Used 2 separate cohorts of breast cancer patients van 't Veer et. al, and Wang et. al. Roughly half had developed metastasis Used Protein-Protein Interaction network obtained by assembling a pooled dataset 57,235 interactions among 11,203 proteins 17

18 Goal: Find Significantly Discriminative Subnetworks Use a scoring system to search for subnetworks highly discriminative of metastasis 18

19 Discriminative Score Function S 19

20 Step 1: Assign activity scores to a subnetwork of genes 20

21 Step 2: Assign discriminative score S to the subnetwork Score(subnetwork) = Mutual Information between a subnetwork s activity score vector and phenotype vector over all patients S(k) = MI (a,c) 21

22 Find Candidate Subnetworks using S and Greedy Search Use a single PPI node as seed At each iteration, add the neighbor resulting in highest score improvement Stop when no addition increases score by rate r=.05, or distance from seed > 2 Report candidate subnetwork and repeat with next node as seed 22

23 Identify Significant Subnets from 3 Null Distributions p1:100 expression perm. trials, p < 0.05 Expression vectors of individual genes randomly permuted on the network p2: 100 random subnetworks seeded at protein i, p < 0.05 p3: 20,000 phenotype perm. trials, p <

24 Results: Correspondence to hallmarks of cancer For two datasets of 295 and 286 patients, 149 and 243 (resp.) discriminative subnets found 47% and 65% of subnets enriched for common biological process 66 and 153 subnets were enriched for processes involved in major events of cancer progression 24

25 Results: Reproducibility Subnetwork markers significantly more reproducible between datasets than individual gene markers 25

26 Results: Reproducibility Dataset 1 Dataset 2 26

27 Results: Reproducibility Shared network motifs with differences in differential expression Left-hand side is from Dataset 1 and righthand side is from Dataset 2 27

28 Results: Subnetwork Markers as Classifiers Averaged expression values for each subnetwork were used as features for a classifier based on logistic regression For comparison, the top individual gene-markers were instead used as features Markers from one dataset were used as predictors of metastasis on the other dataset 28

29 Results: Subnetwork Markers as Classifiers Dataset 1 markers tested on Dataset 2, and vice versa 29

30 Results: Informative of Nondiscriminative Disease Genes Network analyses can identify proteins not differentially expressed, but required to connect higher scoring proteins in a significant subnetwork 85.9 and 96.7% of the significant subnetworks contained at least one protein that was not significantly differentially expressed in metastasis 30

31 Results: Informative of Nondiscriminative Disease Genes Several established prognostic markers were not present in individual gene expression markers, but played a central, interconnecting role in discriminative subnetworks MYC, ERBB2 31

32 Community discovery: motivations Biological networks are modular Metabolic pathways Protein complexes Transcriptional regulatory modules Provide a high-level overview of the networks Predict gene functions based on communities 32

33 Community discovery problem Divide a network into relatively densely connected sub-networks Vertex reorder 33