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Cytoscape and networks. David Amar /~davidama/bioinfo_tutorials. Network biology. Overview: systems biology Represent molecular entities Represent interactions Two main data types Pathways Interaction networks. Biological interaction networks.

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Cytoscape and networks

Cytoscape and networks

David Amar

Network biology
Network biology

  • Overview: systems biology

  • Represent molecular entities

  • Represent interactions

  • Two main data types

    • Pathways

    • Interaction networks

Biological interaction networks
Biological interaction networks

  • Nodes: genes or other molecules

  • Edges: evidence for some interaction – can contain weights, directions

Magtanong et al. 2011 Nature

Biological interaction networks1
Biological interaction networks

  • Nodes: genes/proteins or other molecules

  • Edges based on evidence for interaction

Gene co-expression

Protein-protein interaction

Genetic interaction

Breker and Schuldiner 2009

Voineagu et al. 2011 Nature


  • 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

Cytoscape basics

Cytoscape Basics

Initial window
Initial window

Main Network View, initially blank.

 The toolbar, contains command buttons, the name is shown when the mouse pointer hovers over it.

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 databases1
Load data: import from databases

 The initial window enables searching in the big public databases

Load data import from databases2
Load data: import from databases

 Search example: by gene name

 Choose databases

Import result
Import result

The imported networks by name

Basic statistics

Look at a network
Look at a network

Main Network View

 The toolbar, contains command buttons, the name is shown when the mouse pointer hovers over it.

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
Search for a gene

Information about the marked nodes

Import result1
Import result

The new network

Load data from files
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 files1
Load from files

Contains an interaction network of 331 genes from Ideker et al. 2001 Science

Load data from sif files
Load data: from SIF files

Text: name1<space or tab>interaction_type<space or tab>name2

Load data from a table
Load data: from a table

  • From excel files or tab-delimited text tables

Load data from a table2
Load data: from a table

Set where to look for the nodes and the type

Load data from a table3
Load data: from a table

OPTIONAL: Click on the columns that you want to be kept as “attributes”

Load data owl
Load data: OWL

  • Good for looking at pathways

  • This example: data from the Reactome database

Load data result
Load data: result

Directed edges: signaling


Focus on a selected region (nodes in yellow)

Zoom result
Zoom: result

Move around

Get a sub network1
Get a sub-network

The sub-network was created below the original network

Save the session
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!


  • 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
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:

Circular 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 the scale
Change the scale

Before: scale is 1

Scale is 8


 Open and modify

The intact netowrk node color
The IntActnetowrk: node color

The intact netowrk node color1
The IntActnetowrk: node color

 Node color

 Each column represents some information that we have

Discrete: set a value for each type of information


  • 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
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
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 2: use ClusterViz

Step 3 look at the results
Step 3: look at the results

All clusters

Sorted by size

Select a cluster

Step 4 biological function
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
Step 4: BINGO

Select all nodes (Ctrl+A)

Step 4 bingo1
Step 4: BINGO

Give the cluster a name (“Cluster 1”)

Select human

Step 4 results
Step 4: Results

Summary table

GO graph

Mark in the network

Only correted p-values matter!!!

Ii analyze a gene set
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
Resources and installation

  • Installing GeneMANIA may take >30 minutes

  • Steps

  • Apps -> Apps Manager

  • Install GeneMANIA

  • Open GeneMANIA (Apps->GeneMANIA)

    • Confirm data download

    • A new window will open: select human for this tutorial


  • Our input: a set of genes from Hauser et al. 2005 (


Genemania input window
GeneMANIA: input window

Paste here the gene names (or ids) separated by spaces (no commas)

Genemania input window1
GeneMANIA: input window

Genemania input window2
GeneMANIA: input window

The recognized genes and their full names

For each interaction type there is a list of networks that can be marked

The type of the supported networks

Genemania input window3
GeneMANIA: input window

Use physical interactions, pathways and co-expression for our example


The output network. Grey nodes are new genes that were added to improve the connectivity

Information tables. For example: the detected functions


Layout was modified to organic for better visualization

Mark a function: automatically marks the relevant nodes

Iii analyze different interaction types
III) Analyze different interaction types…

  • “Positive” – expected within families

  • “Negative” – expected between families

  • Some networks contain both

Members of protein complex

Members of parallel pathways


Analysis of network pairs
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. 2008

    Kelley and Kingsford 2011

    Leiserson et al. 2011

Solution modmap
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
Load ModMap analysis

  • Our example: combined analysis of yeast PPI and GI data

  • Find GI among complexes

  • Load the network: type interaction types

  • Load the association of nodes to modules

  • Color the results and the set layout

Load the network
Load the network

  • Load the YeastData.xlsx file

Important, we have several types

Load the network1
Load the network

  • Load the YeastData.xlsx file

The network is large, we tell Cytoscape to generate it

Load a clustering solution
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 solution1
Load a clustering solution

Right click and give it a name

Load a clustering solution2
Load a clustering solution

Right click and give it a name

Layout a clustering solution results
Layout a clustering solution: results

A circle for each cluster

Unclustered nodes

Remove unclustered nodes
Remove unclustered nodes

Mark the selected nodes and create a sub-network

Zoom in on a part of the solution
Zoom in on a part of the solution

Not informative enough, we cannot see edge types…

Iv overlay gene expression data
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