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BIMM 143 Biological Network Analysis Lecture 17

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BIMM 143 Biological Network Analysis Lecture 17 Networks can be used to model many types of biological data BIMM 143 Biological Network Analysis Lecture 17 Barry Grant http://thegrantlab.org/bimm143 TODAYS MENU: TODAYS MENU: ‣Network introduction ‣Network introduction ‣Network visualization ‣Network visualization ‣Network analysis ‣Network analysis ‣Hands-on: ‣Hands-on: Cytoscape and R (igraph) software tools Cytoscape and R (igraph) software tools for network visualization and analysis for network visualization and analysis 3/7/17 10 Pathway and Network Analysis [Yeast, cell-cycle PPIs] 3/7/17 • Any analysis involving pathway or network informa2on Biological Networks • Most commonly applied to interpret lists of genes Represent biological interactions • Most popular type is pathway enrichment analysis, but • ➡ Physical, regulatory, genetic, functional, etc. many others are useful Useful for discovering relationships in big data • Helps gain mechanis2c insight into ‘omics data • 12 ➡ Better than tables in Excel Types of Pathway/Network Analysis • Visualize multiple heterogenous data types together ➡ See interesting patterns 3/7/17 • Network analysis ➡ Well established quantitive metrics from graph theory Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca 12 11 Types of Pathway/Network Analysis Pathways vs Networks - Detailed, high-confidence consensus - Simplified cellular logic, noisy - Biochemical reac2ons - Abstrac2ons: directed, undirected - Small-scale, fewer genes - Large-scale, genome-wide - Concentrated from decades of literature - Constructed from omics data integra2on Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca 13 Types of Pathway/Network Analysis 4 Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca Are new pathways How are pathway 13 Types of Pathway/Network Analysis altered in this activities altered in cancer? Are there a particular What biological clinically-relevant patient? Are there processes are tumour subtypes? targetable altered in this pathways in this cancer? patient? Are new pathways How are pathway altered in this activities altered in cancer? Are there a particular What biological clinically-relevant patient? Are there Module 12: Introduc0on to Pathway and Network Analysis processes are tumour subtypes? biotargetableinformatics .ca altered in this pathways in this cancer? patient? 5 Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca 5 3/7/17 Types of Pathway/Network Analysis 12 3/7/17 12 Types of Pathway/Network Analysis Applications of Network Biology • Gene Function Prediction – shows connections to sets of genes/proteins involved in same biological process • Detection of protein complexes/other modular structures – discover modularity & higher order organization (motifs, jActiveModules, UCSD feedback loops) • Network evolution – MCODE, University of Toronto biological process(es) conservation across species • Prediction of new interactions and functional associations – PathBlast, UCSD Statistically significant domain- domain correlations in protein interaction network to predict What biological process is Are NEW pathways altered protein-protein or genetic interaction; allostery in altered in this cancer? in this cancer? Are there clinically DomainGraph, Max Planck Institute Module 12: Introduc0on to Pathway and Network Analysis bioinformaticsmolecular.ca networks relevant tumor subtypes? Slide from: humangenetics-amc.nl Applications of Network Informatics in Types of Pathway/Network Analysis Disease What’s13 missing • Identification of disease subnetworks – identification of disease network • Dynamics subnetworks that are transcriptionally active in ➡ Pathways/networks represented as static processes Module 12: Introduc0on to Pathway and Network Analysis disease (disease sub-types). bioinformatics.ca ➡ Difficult to represent a calcium wave or a feedback • Subnetwork-based loop diagnosis – source of Agilent Literature Search biomarkers for disease ➡ More detailed mathematical representations exist classification, identify interconnected genes whose that handle these e.g. Stoichiometric modeling, aggregate expression levels are predictiveAre of diseasenew state pathways How areKinetic pathway modeling (VirtualCell, E-cell, …) PinnacleZ, UCSD 13 • Subnetwork-basedaltered gene in this • Detail – atomic structures & exclusivity of interactions. Types of Pathway/Network Analysis association – map common activities altered in pathway cancer?mechanisms affected Are there by collection of genotypes a •particularContext – cell type, developmental stage Mondrian, MSKCC clinically-relevant What biological Slide from: humangenetics-amc.nl patient? Are there June 2009 processes are tumour subtypes? targetable altered in this pathways in this cancer? patient? Are new pathways How are pathway altered in this activities altered in cancer? Are there a particular What biological clinically-relevant patient? Are there Module 12: Introduc0on to Pathway and Network Analysis processes are tumour subtypes? biotargetableinformatics .ca altered in this pathways in this cancer? patient? 5 Module 12: Introduc0on to Pathway and Network Analysis bioinformatics.ca 5 What have we learned so far… TODAYS MENU: • Networks are useful for seeing relationships in large ‣Network introduction data sets ➡ Important to understand what the nodes and edges ‣Network visualization mean ➡ Important to define the biological question - know what ‣Network analysis you want to do with your gene list or network • Many methods available for network analysis ‣Hands-on: ➡ Good to determine your question and search for a Cytoscape and R (igraph) software tools solution ➡ Or get to know many methods and see how they can be for network visualization and analysis applied to your data Network Visualization Outline Network representations • Network representations • Automatic network layout • Visual features • Visually interpreting a network 1 2 3 List of relationships Network view Adjacency matrix view Network view is most useful when network is sparse! Automatic network layout • Modern graph layouts are optimized for speed and aesthetics. In particular, they seek to minimize overlaps and edge crossing, and PKC (Cell Wall Integrity) ensure similar edge length across the graph. WSC2 WSC3 MID2 Wsc1/2/3 Mid2 SLG1 Rho1 RHO1 Pkc1 PKC1 Bni1 BNI1 Bck1 Polarity BCK1 MKK1 MKK2 Force-directed layout:Mkk1/2 Dealing with ‘hairballs’: zoom or filter SLT2 Nodes repel and edges pull Zoom Slt2 Focus • Good for up to 500 nodes PKC (Cell Wall Integrity) WSC2 WSC3 MID2 ➡ Bigger networks give hairballs Wsc1/2/3 Mid2 ➡ Reduce number of edges SLG1 Rho1 RHO1 ➡ Or just use a heatmap for dense networksSwi4 /6 Rlm1 SWI4 SWI6 RLM1 Bni1 PKCPkc1 Cell PKC1 • Advice: try force directed first, or hierarchical for tree-like BNI1 networks Wall Bck1 Polarity Integrity BCK1 Synthetic Lethal • Tips for better looking networks Transcription Factor Regulation MKK1 MKK2 ➡ Manually adjust layout Protein-Protein Interaction Mkk1/2 ➡ Load network into a drawing program (e.g. Illustrator) SLT2 Up Regulated Gene Expression Slt2 and adjust labels Down Regulated Gene Expression Swi4/6 Rlm1 SWI4 SWI6 RLM1 Synthetic Lethal Transcription Factor Regulation Protein-Protein Interaction Up Regulated Gene Expression Down Regulated Gene Expression Visual Features Visually Interpreting a Network • Node and edge attributes – Text (string), integer, float, Boolean, list – E.g. represent gene, interaction attributes • Visual attributes – Node, edge visual properties – Color, shape, size, borders, What to look for: opacity... Data relationships Guilt-by-association Dense clusters Global relationships What have we learned so far… TODAYS MENU: ‣ Network introduction • Automatic layout is required to visualize networks ‣ Network visualization • Networks help you visualize interesting relationships in your data ‣ Network analysis • Avoid hairballs by focusing analysis ‣ Hands-on: • Visual attributes enable multiple types of data to • Cytoscape and R (igraph) software tools be shown at once – useful to see their for network visualization and analysis relationships Introduction to graph theory • Biological network analysis historically originated from the tools and concepts of social network analysis and the application of graph theory to the social sciences. • Wikipedia defines graph theory as: ➡ “[…] the study of graphs used to model pairwise relations between objects. A graph in this context is made up of vertices connected by edges”. • In practical terms, it is the set of concepts and methods that can be used to visualize and analyze networks Types of network edges • Every network can be expressed mathematically in the form of an adjacency matrix Connection, There is directional Edges can also without a given flow/signal implied have weight ‘flow’ implied (e.g. protein A (e.g. metabolic or (i.e. a ‘strength’ of binds protein B) gene networks) interaction). Network topology • Topology is the way in which the nodes and edges are arranged within a network. • The most used topological properties and concepts include: ➡ Degree (i.e. how may node neighbors) ➡ Communities (i.e. clusters of well connected nodes) ➡ Shortest Paths (i.e. shortest distance between 2 nodes) ➡ Centralities (i.e. how ‘central’ is a given node?) ➡ Betweenness (a measure of centrality based on shortest paths) Random graphs vs scale free Analyzing the topological features of a network is a useful way of identifying relevant participants
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