David Amar

Network biology Overview: systems biology Represent molecular entities Represent interactions Two main data types Pathways Interaction networks

Biological interaction networks Nodes: genes or other molecules Edges: evidence for some interaction – can contain weights, directions Magtanong et al Nature

Biological interaction networks Nodes: genes/proteins or other molecules Edges based on evidence for interaction Voineagu et al Nature Breker and Schuldiner 2009 Gene co-expressionProtein-protein interaction Genetic interaction 4

Cytoscape Cytoscape is an open source software for integrating, visualizing, and analyzing networks. This tutorial describes the Cytoscape 3 user interface. Outline Basics Load and visualize data Customize Applications Clustering Enrichment analysis GeneMANIA Modmap Gene expression analysis

Initial window The toolbar, contains command buttons, the name is shown when the mouse pointer hovers over it. Main Network View, initially blank. Control Panel: lists the available networks by name Network Overview Pane Table Panel: can be used to display node, edge, and network table data

Load data: import from databases

The initial window enables searching in the big public databases

Load data: import from databases Search example: by gene name Choose databases

Import result The imported networks by name Basic statistics

Look at a network The toolbar, contains command buttons, the name is shown when the mouse pointer hovers over it. Main Network View Control Panel: lists the available networks by name Network Overview Pane: move around! Table Panel: displays node, edge, and network table data

Search for a gene Information about the marked nodes

Load data: import all interactions

Import result The new network

Load data: from files We sometimes have our own data From papers A special search in a database Our experiment (e.g., correlation between genes) Famous formats SIF A table OWL – for pathways, “complex” text But easy to get and very informative once uploaded

Load from files

Contains an interaction network of 331 genes from Ideker et al Science

Load data: from SIF files Text: name1 interaction_type name2

Load data: from a table From excel files or tab-delimited text tables

Load data: from a table

Set where to look for the nodes and the type

Load data: from a table OPTIONAL: Click on the columns that you want to be kept as “attributes”

Result

Load data: OWL Good for looking at pathways This example: data from the Reactome database

Load data: result Directed edges: signaling

Zoom

Focus on a selected region (nodes in yellow)

Zoom: result Move around

Get a sub-network

The sub- network was created below the original network

Save the session We imported six networks Before we start modifying them lets save the session File -> Save Sanity check: close Cytoscape and load the session!

Remarks At this point we know to load data from databases and files We can perform simple navigation, zoom and save We saved different networks each its own visualization ‘rules’ A good habit that saves troubles: save a session for each visualization type Multiple networks, but keep a consistent visualization

Modifying and saving a visualization Cytoscape supports countless options Layouts Node size, color, label… Edge width, line type… We will show main examples that are enough to start To save the graph as an image:

Change the layout

Organic layout

Circular layout Places all of the nodes in a circular arrangement. Very quick Partitions the network into disconnected parts and independently lays out those parts.

Force-directed Uses physical simulation that models the nodes as physical objects and the edges as springs connecting those objects together.

Change layout scale

Change the scale Before: scale is 1 Scale is 8

Style Open and modify

The IntAct netowrk: node color

Node color Each column represents some information that we have Discrete: set a value for each type of information

Apps Cytoscape also has many tools called ‘apps’ Install by going to Apps -> App Manager Applications support Advanced analysis Biological analysis Integrating data Import special data

I) Find and annotate dense areas Use an app that “clusters” the network Biological assumption We look for protein communities Many interactions within Probably share function Gene function prediction

Step 1: remove duplicated edges Sometimes nodes are linked by more than one edge Multiple evidence for interaction Remove them for clustering and simpler visualization

Step 2: use ClusterViz

Step 3: look at the results All clusters Sorted by size Select a cluster

Step 3: look at the results

Step 4: biological function? We discovered a cluster A set of highly connected proteins What biological processes/functions are enriched in this cluster? Discover significantly over-represented biological functions Compared to creating random clusters

Step 4: BINGO Select all nodes (Ctrl+A)

Step 4: BINGO Give the cluster a name (“Cluster 1”) Select human

Step 4: Results Summary tableGO graph Only correted p-values matter!!! Mark in the network

II) Analyze a gene set We have a set of genes we want to interpret From papers From data analysis We want to discover Functional enrichments How they interact within themselves and similar genes Use GeneMANIA

Resources and installation Installing GeneMANIA may take >30 minutes Steps 1. Apps -> Apps Manager 2. Install GeneMANIA 3. Open GeneMANIA (Apps->GeneMANIA) 1. Confirm data download 2. A new window will open: select human for this tutorial

GeneMANIA Our input: a set of genes from Hauser et al ( ) HSPA1B, HSPA1A, DNAJC6, DNAJB2, UBE1, PARK5, SLC25A5, COX5B, COX6C, NDUFA3, ATP5I, HK1, COX4I1, ATP1B1, COX6B, SLC25A3, NDUFS5, ATP5O, UQCRH, ATP5C1, NDUFB8, ATP5G3, ATP5C1, VDAC3, COX4I1, COX7B, NDUFA9, ATP1B1, ATP6V0A1, ATP6V0D1, ATP6V0C, ATP6V1B2, SLC9A6, ATP61P1, ATP6V1D, ATP6V0B, ATP6V1A1, ATP6V1E1, GDI1, STXBP1, SYT1, VAMP1

GeneMANIA: input window Paste here the gene names (or ids) separated by spaces (no commas)

GeneMANIA: input window

The recognized genes and their full names The type of the supported networks For each interaction type there is a list of networks that can be marked

GeneMANIA: input window Use physical interactions, pathways and co-expression for our example

Results Information tables. For example: the detected functions The output network. Grey nodes are new genes that were added to improve the connectivity

Results Mark a function: automatically marks the relevant nodes Layout was modified to organic for better visualization

VS.

Highlight specific interactions

III) Analyze different interaction types… “Positive” – expected within families “Negative” – expected between families Some networks contain both VS. Members of protein complex Members of parallel pathways

Analysis of network pairs Interactions types can differ: within (“positive”) vs. between (“negative”) functional units Input: networks H,G with same vertex set Goal: summarize both networks in a module map Node – module: gene set highly connected in H Link – two modules highly interconnected in G Between-pathway models Kelley and Ideker 2005 Ulitsky et al Kelley and Kingsford 2011 Leiserson et al

Solution: ModMap Cytoscape app: under construction Currently: run the command line tool and upload to Cytoscape as a solution We will show how to upload a solution

Load ModMap analysis Our example: combined analysis of yeast PPI and GI data Find GI among complexes 1. Load the network: type interaction types 2. Load the association of nodes to modules 3. Color the results and the set layout

Load the network Load the YeastData.xlsx file Important, we have several types

Load the network Load the YeastData.xlsx file The network is large, we tell Cytoscape to generate it

Load a clustering solution Modmap_modules.txt file format (text file): Node module_name Import Table: a way to add external information about the nodes

Load a clustering solution Right click and give it a name

Load a clustering solution Right click and give it a name

Load a clustering solution

Layout a clustering solution

Layout a clustering solution: results Unclustered nodes A circle for each cluster

Remove unclustered nodes Mark the selected nodes and create a sub-network

Remove self and duplicated edges

Zoom in on a part of the solution Not informative enough, we cannot see edge types…

Change the visualization style

IV) Overlay gene expression data Class/Home exercise (data in the exp_data directory) Load human PPI Load gene fold-change in a gene expression experiment Set node color and size by the fold change Play with the layout For example, group attribute layout Run BINGO on a selected sub-network