Cytoscape and networks
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Cytoscape and networks. David Amar http://tau.ac.il /~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

http://tau.ac.il/~davidama/bioinfo_tutorials


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

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


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 databases

Load data: import from databases


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


Load data import all interactions

Load data: import all interactions


Load data import all interactions1

Load data: import all interactions


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 files

Load from files


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 table1

Load data: from a table


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”


Result

Result


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


Cytoscape and networks

Zoom


Cytoscape and networks

Zoom

Focus on a selected region (nodes in yellow)


Zoom result

Zoom: result

Move around


Get a sub network

Get a sub-network


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!


Remarks

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

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

Change the layout


Organic layout

Organic layout


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

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 layout scale


Change the scale

Change the scale

Before: scale is 1

Scale is 8


Style

Style

 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


Applications

Applications


Cytoscape and networks

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

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 3 look at the results1

Step 3: look at the results


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


Genemania

GeneMANIA

  • Our input: a set of genes from Hauser et al. 2005 (http://archneur.ama-assn.org/cgi/pmidlookup?view=long&pmid=15956162)

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

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


Results

Results

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

Information tables. For example: the detected functions


Results1

Results

Layout was modified to organic for better visualization

Mark a function: automatically marks the relevant nodes


Cytoscape and networks

VS.


Highlight specific interactions

Highlight specific interactions


Highlight specific interactions1

Highlight specific interactions


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

VS.


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


Load a clustering solution3

Load a clustering solution


Layout a clustering solution

Layout a clustering solution


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


Remove self and duplicated edges

Remove self and duplicated edges


Zoom in on a part of the solution

Zoom in on a part of the solution

Not informative enough, we cannot see edge types…


Change the visualization style

Change the visualization style


Change the visualization style1

Change the visualization style


Change the visualization style2

Change the visualization style


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


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