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

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

  • Nodes: genes or other molecules

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

Magtanong et al. 2011 Nature


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 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


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

 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

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

Information about the marked nodes


Load data: import all interactions


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


Load from files

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


Load data: from SIF files

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


Load data: from a table

  • From excel files or tab-delimited text tables


Load data: from a table


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


Zoom

Focus on a selected region (nodes in yellow)


Zoom: result

Move around


Get a sub-network


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 IntActnetowrk: node color


The IntActnetowrk: node color

 Node color

 Each column represents some information that we have

Discrete: set a value for each type of information


Applications


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 table

GO graph

Mark in the network

Only correted p-values matter!!!


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

  • Apps -> Apps Manager

  • Install GeneMANIA

  • Open GeneMANIA (Apps->GeneMANIA)

    • Confirm data download

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


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

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


GeneMANIA: input window


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 window

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


Results

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

Information tables. For example: the detected functions


Results

Layout was modified to organic for better visualization

Mark a function: automatically marks the relevant nodes


VS.


Highlight specific interactions


Highlight specific interactions


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

  • 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

  • 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

  • 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 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

A circle for each cluster

Unclustered nodes


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


Change the visualization style


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


  • Login