Network analysis. Martina Kutmon Department of Bioinformatics Maastricht University

Transcription

1 Network analysis Martina Kutmon Department of Bioinformatics Maastricht University

2 What's gonna happen today? Network Analysis Introduction Quiz Hands-on session ConsensusPathDB interaction database

3

4 Outline 1. Terminology 2. Examples of networks 3. Network properties 4. Use cases in biology 5. Interaction data 6. Introduction practical session - Cytoscape

5 1. Terminology

6 Terminology Nodes and Edges nodes are the objects in the network edges are links and interactions in the network Node Edge Node

7 Terminology Graph vs. Network graph = generic term for a mathematical concept of a set of nodes connected by links called edges network = graph = representation of a set of objects where some pairs of objects are connected by links V = {1, 2, 3, 4, 5, 6} E = {{1, 2}, {1, 5}, {2, 3}, {2, 5}, {3, 4}, {4, 5}, {4, 6}}.

8 Terminology Neighbor a neighbor in a network is a node that is linked through a direct edge A Edge A is a neighbor of B B is a neighbor of A and C C is a neighbor of B B Edge C

9 Terminology Path a path is a sequence of edges which connect a sequence of nodes A Path from C to E: C --> B --> D --> E length = 3 B C D E

10 2. Examples of networks

11 Networks everywhere

12 Networks everywhere Seven Bridges of Königsberg Leonhard Euler, 1736 Euler proved that this problem can't be solved and laid the foundations of graph theory.

13 Networks everywhere

14 Networks everywhere

15 Networks everywhere

16 Networks everywhere

17 Networks everywhere

18 Networks everywhere

19 Biological networks Metabolic networks Gene networks Protein networks Nodes Metabolites Nodes TFs, target genes Nodes Proteins Edges Enzymatic conversions Edges Transcriptional interaction Edges Physical / functional interaction

20 3. Network, Node and Edge Properties

21 Network properties Directed vs. undirected

22 Network properties Cyclic vs. acyclic

23 Network properties Weighted vs. unweighted

24 Node Properties How influential is a person in a social network? How important is a room in a building? How important is a transcription factor in a biological process? How much influence has a mutation in a protein?

25 Node Properties Degree centrality: number of edges incident upon a node Undirected: only node degree! Directed: in vs. out degree

26 Node Properties Degree centrality: number of edges incident upon a node

27 Node Properties Degree centrality Biological interpretation: nodes with a high degree are often essential elements in the network transcription factors have a high out-degree studies showed that proteins with a high degree are more likely to be essential for survival

28 Node Properties Closeness centrality: the inverse of the average of the closest paths to all other nodes C(n) = 1 / avg( L(n,m) )) F -> A = 2 F -> B = 3 F -> D = 2 F -> C = 1 F -> E = 2 (F - C - A) (F - C - D - B) (F - C - D) (F - C) (F - C - E) avg(l(n,m)) = ( ) /5 = 2 C(n) = 1 / 2 = 0.5

29 Node Properties Closeness centrality: the inverse of the average of the closest paths to all other nodes C(n) = 1 / avg( L(n,m) )) C -> A = 1 C -> B = 2 C -> D = 1 C -> F = 1 C -> E = 1 avg(l(n,m)) = ( ) /5 = 1.2 C(n) = 1 / 2 = 0.833

30 Node Properties Closeness centrality Biological interpretation: indication for how fast information spreads from a given node to other reachable nodes in the network the more central a node is, the lower is the distance to all other nodes, the higher is the closeness

31 Node Properties Betweenness centrality: Cb(n) = s n t (σst (n) / σst) - calculates the node betweenness for node n - σst = number of shortest path from s to t - σst (n) = number of shortest path from s to t that go through n - repeat this for each node pair in the network

32 Node Properties Betweenness centrality Biological interpretation: amount of control that this node exerts over the interactions of other nodes in the network how much information load is on the node connectivity of the network hubs that connect to subnetworks Applies to edges as well!

33 4. Use Cases in Biology

34 Identification of hubs Hub elements tend to be essential Often involved in multiple processes Important for the overall connectivity of the network Brodsky, Igor E., and Ruslan Medzhitov. "Targeting of immune signalling networks by bacterial pathogens." Nature cell biology 11.5 (2009):

35 Identification of hubs Brodsky, Igor E., and Ruslan Medzhitov. "Targeting of immune signalling networks by bacterial pathogens." Nature cell biology 11.5 (2009):

36 Clustering Grouping a set of objects in such a way that objects within a cluster are more similar (in some sense of another) to each other than to those in other clusters. Find objects that behave similar, have similar properties, are close to each other,...

37 Clustering Tan, Kai, et al. "Transcriptional regulation of protein complexes within and across species." Proceedings of the National Academy of Sciences (2007):

38 Network Motifs Network motifs -> significantly recurring connected subnetwork Types of networks show different motifs Wang, Edwin, and Enrico Purisima. "Network motifs are enriched with transcription factors whose transcripts have short halflives." Trends in Genetics21.9 (2005):

39 Active Subnetworks Search for expression activated subnetworks Connected parts in the network that show significant changes in expression over particular subsets of conditions

40 Active Subnetworks

41 Active Subnetworks Breast cancer data Basal subtype of the cancer Identification of positive feed forward loops Gaire, Raj K., et al. "Discovery and analysis of consistent active sub-networks in cancers." BMC bioinformatics 14.Suppl 2 (2013): S7.

42 Data Integration Big challenge in network biology: data integration increased complexity proteins, metabolites, genes SNP data, transcripts, micrornas, cells,...

43 Network Inference A lot of different methods Most commonly used = correlation networks Predicting the "wiring diagram" of the network Calculate the correlation between two genes based on their expression Use correlation between genes as edges 20,000 genes -> ~8GB data -> ~3 hrs calculation time

44 Network Inference Correlation does not tell you the direction of the edge! Data derived networks Combine with other interaction data like IntAct or STRING

45 5. Interaction Data

46 PathGuide pathway data interaction data IntAct, STRING, MINT, Pazar, mirecords, microcosm, DrugBank, BIND,...

47 IntAct

48 STRING

49 mirecords

50 5. Introduction Quiz and Hands-on Session

51 Quiz Work alone or in groups Get familiar with the tools

52 Cytoscape Network visualization and analysis tool > 100 plugins (now called apps) available for additional functionality Not only biological networks, but also social sciences, semantic web, complex network analysis

53 Cytoscape Cytoscape with plugins (or now called apps) Haven't installed Cytoscape yet? Ask for a USB stick.

54 Cytoscape

55 Cytoscape plugins / apps

56 Open Network Always: File -> Import!

57 NetworkMerge Plugins -> Advanced Network Merge

58 NetworkAnalyzer Calculates all the node properties in the network

59 NetworkAnalyzer

60 Layouts Grid Layout Circular Layout

61 Layouts Hierarchical Layout Forced-Directed Layout

62 VizMapper - change the visual style color your nodes (e.g. based on expression)

63 VizMapper - change the visual style add icons on your nodes

64 VizMapper - change the visual style change the background color

65 VizMapper - change the visual style change node size and colors base on node properties

66 VizMapper - change the visual style pie charts on nodes

67 VizMapper - change the visual style highlight a subnetwork

68 Hands on session 1. Analyze a protein interaction network from STRING 2. Load a pathway from WikiPathways and extend it with regulatory interactions (micrornas, TFs and drugs) -> CyTargetLinker plugin 3. Find interactions between a group of genes visualize expression data on the network (MiMI plugin)

69 CyTargetLinker Integrate regulatory interactions into network analysis Generic and flexible - different regulatory interactions can be integrated together TF-gene interactions from ENCODE, microrna-target from mirtarbase and mirecords and drug-targets from DrugBank

70 CyTargetLinker Results panel shows how many interactions have been added Colors of the edges indicate from which resource the interaction comes from (data is extracted from so called RINs - regulatory interaction networks)

71 MiMI Michigan Molecular Interactions Find interactions between query genes (and first neighbors) Only supports query by gene name (since many genes have synonyms, genes might not return any result)

72 Hands on session After quiz and coffee Instructions available on projects.bigcat.unimaas.nl/ebi-roadshow/

73 Thanks to Anwesha Egon

74 Questions?