cytoscape

Integrative Bioinformatics using Cytoscape(and R2)

(Bio)Chemistry versus Molecular Biology

…some basic concepts(Bio)Chemistry

• Concentrations• Molecular structures• Reaction equations• Quantitative• Defined experimental setup

Molecular Biology• Regulation• Large biomolecules• Large scale processes• Qualitative• Complex experimental setup (by

necessity!)

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Molecular Biology: New techniquesIntegrative Bioinformatics needed

(Deep)Sequencing – Arrays – Proteomics

• Quantitative analysis– handling large datasets– statistics

• Capturing complexity– integration– graphs

• Integrative Bioinformatics: Integrated Bioinformaticians!

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Integrative Bioinformatics: An example

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Integrative Bioinformatics: What they did

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1. Sequence genome; assign gene function using protein sequence, structural similarities (Bonneau et al., 2004; Ng et al., 2000)

2. Perturb cells: environmental factors; knockouts (Baliga et al., 2004; Kaur et al., 2006; Kottemann et al., 2005)

3. Measure changes: microarrays (Baliga et al., 2004;Kaur et al., 2006; Whitehead et al., 2006).

4. Integrate diverse data (mRNA levels, evolutionarily conserved associations among proteins, metabolic pathways, cis-regulatory motifs, etc.) with the cMonkey algorithm to reduce data complexity and identify subsets of genes that are coregulated in certain environments (biclusters) (Reiss et al., 2006).

5. Using the machine learning algorithm Inferelator construct a dynamic network model for influence of changes in EFs and TFs on the expression of coregulated genes (Bonneau et al., 2006).

6. Explore the network with Gaggle, a framework for data integration and software interoperability to formulate and then experimentally test hypotheses to drive additional iterations of steps 2–6 (Shannon et al., 2006)

Integrative Bioinformatics: Their framework

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Integrative Bioinformatics: results

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Goes to show that:

1. Aggregate

2. Search/Visualize

3. Analyze/Feedback

• Combine data from different sources

• Filter

• Algorithms

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Need for adaptable software

Goal: Facilitate ideas

Cytoscape - Network Visualization and Analysis

• Freely-available (open-source, java) software, easily extensible (Plugin API)

• Visualizing networks (e.g. molecular interaction networks)

• Analyzing networks with gene expression profiles and other cell state data (GO, proteomics, …)

• Used in several hundred analyses in recent literature

• Continuity guaranteed

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An example Cytoscape work-flow

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Cytoscape Workflow

1. Load Networks (Import network data into Cytoscape)

2. Load Attributes (Get data about networks into Cytoscape)

3. Analyze and Visualize Networks 4. Prepare for Publication• A specific example of this workflow:

– Cline, et al. “Integration of biological networks and gene expression data using Cytoscape”, Nature Protocols, 2, 2366-2382 (2007).

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Networks as graphs• A Network is a collection of

– Nodes (or vertices)– Edges connecting nodes

(directed or undirected, weighted, multiple edges, self-edges)

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Nodes can represent proteins, genes, metabolites, or groups of these (e.g. complexes) - any sort of object

Edges can be either physical or functional interactions, activators, regulators, reactions - any sort of relations

Cytoscape Workflow1. Load Networks (Get network data into Cytoscape)


3. Analyze and Visualize Networks 4. Prepare for Publication

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Creating a network

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Free-format Text and Excel Files

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Specify Input File

Define Columns

Text Parsing Options

Preview

Pathways: plenty resources

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http://pathguide.org : over 240 pathway db’s

All kinds of network data…

• Physical interactions– Protein – Protein interactions – Protein – DNA interactions– Metabolic interactions

• Functional interactions– Co-expression relations– Genetic interactions– Knockout/siRNA – targets

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Pre-formatted Network Files • Cytoscape supports many popular file formats:

SIF (Simple Interaction Format)

GML (Graph Markup Language)

XGMML (eXtensible Graph Markup and Modeling Language)

BioPax (Biological Pathway Data)

PSI-MI 1 & 2.5 (Protein Standards Initiative)

SBML Level 2 (Systems Biology Markup Language)

• Available for download from data sources (URLs, web-services, formatted table files)

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Internet Databases• Cytoscape version 2.6

– web service clients: import networks directly from several trusted internet resources

IntAct (MBL-EBI)

PathwayCommons (collection of data resources)

NCBI Entrez Gene Many more will be included...

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Interaction Database Search

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Import

Visualize and Analyze




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What are Attributes?• Any data that describes or provides details about the nodes

and edges in the network– Gene Expression Data– Mass Spectrometry Data– Protein Structure Information– Gene Ontology (GO) terms– Interaction Confidence Values, etc

• Cytoscape support multiple data types– Numbers (integers, floats) – Text (strings) – Logical (booleans)– Lists…

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Attribute Management

Node or Edge ID

Specific Attribute Tabs

Select Attributes for Display

Strings and floating type of attributes

Load Attributes:Import Attribute Files

• Map data about Networks onto Networks.• Attributes can be loaded in many of the same

ways as networks.Import pre-formatted attribute filesImport formatted text or Excel filesCreate attributes manually in attribute editorLoad attributes from web servicesID mapping though node attributes

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ID Mapping

• Mapping identifiers from one source to another is a major challenge

• Multiple levels of IDs E.g. probe->gene ->peptide->protein

• Cytoscape provides an ID mapping through the BioMart web service of EBI to convert the IDs

• Not perfect but sufficient• Additional mapping mechanism

underway

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Visual Data Integration

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1. Network Data

2. Attribute Data

YDR382W pp YDL130WYDR382W pp YFL039CYFL039C pp YCL040WYFL039C pp YHR179W

ExpressionValueYCL040W = 0.542YDL130W = -0.123YDR382W = -0.058YFL039C = 0.192YHR179W = 0.078

VizMapper

VizMapper

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List of Data Attributes

Default Visual Style Editor

List of Visual Attributes

Mapping definition

List of Visual Styles

Types of mappings• Continuous

Continuous Data mapped to Continuous Visual Attributes (e.g. gene expression levels mapped to node color)

Continuous Data mapped to Discrete Visual Attributes (e.g. p-value categories mapped to node shape)

• Discrete Discrete (categorical) Data to Discrete Visual Attributes (e.g. GO annotation

mapped to node shape) Discrete Data mapped to Continuous Visual Attributes(e.g. multiple GO terms

mapped to pie coloring)

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Network Filtering

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Several Layout Algorithms

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Spring-embeddedCircular

Hierarchical

Linkout

• Nodes and Edges act as hyperlinks to external databases.

• User-configurable URLs

• Collection of the biological results for the publication

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Prepare for Publication• Fine tune the

Figures• Manual Layout

manipulation options (align, scale, rotate)

• Manually override visual styles

–place labels, change colors, etc.

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Finalizing the Figures• Publication Quality

Graphics in several formatsPDF, EPS, SVG,

PNG, JPEG, and BMP

• Export Session to HTML for Web

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Cytoscape: So what?

The big Pro Cyto argument: EXTENSIBLE• Plugins, Plugins, Plugins

– In our case enabled extended array data analysis

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Cytoscape is Extensible

• Cytoscape is open source and free software • A plugin interface that allows any programmer to write their

own extensions to Cytoscape• Plugins represent the primary biological analysis mechanism in

Cytoscape• Plugins are distributed from a central Cytoscape database and

can be installed while running• Several dozens of plug-ins currently available

(www.cytoscape.org/plugins/index.php)

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Hello World Plugin

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http://cytoscape.org/cgi-bin/moin.cgi/Hello_World_Plugin

http://cytoscape.org/cgi-bin/moin.cgi/Developer_Homepage

Extending the workflow through plugins

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Graph based integration and analysis of molecular biological data

Integrative Bioinformatics in our group

• Aggregate data: 18000+ Affymetrix arrays– Tumor series– Public data– Experiments

• Manipulate celllines; Lentiviral library• Search/Visualize/Selection: R2

– Statistical cutoffs – Correlations: R2– Clinical data coupling

• Analysis/Feedback: R2 and Cytoscape– Known Interactions– Transcription Factor binding

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Integrative Bioinformatics in our group

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Externaldata sources

Statistical analysisPerl module

Cytoscape webstart

AMC Plugin

Canonicalpaths

DB

Patient dataGEO arrays

Algorithms

Array data: Tumor and Experiments

R2-array analysis interface

Cytoscape interface

HGServer

http://www.r-project.org/index.html

Array data analysis: R2

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R2 interface: Demo

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R2 interface

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R2 interface

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R2 interface

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R2 interface

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R2 interface

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Timeseries in R2 / Cytoscape (Demo)

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Timeseries in R2

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Timeseries in R2

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Timeseries in R2Integration with Cytoscape

through webstart

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Timeseries in Cytoscape: Visualization

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Timeseries in Cytoscape: Aggregate data

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Timeseries in Cytoscape: Search/Filter

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Timeseries in Cytoscape: Filter

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Timeseries in Cytoscape

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Timeseries in Cytoscape

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Tf (green) and partners (red)

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Filtering

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Filtering

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Coloring, layout

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Resuming:

1. Aggregate

2. Search/Visualize

3. Analyze/ Feedback

• Combine NOTCH3 knockout data with TF and PPi data

• Layout timeseries/Find downstream targets

• Identify MSX1/Knockout in new experiment

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More Plugin Examples• BiNGO (Enriched GO categories found in the sub-network)

• WikiPathways (Visualize curated pathways)

• MCODE (Putative protein complexes)

• GenePro (Protein-Protein interaction cluster visualization)

• jActiveModules (Search for significant sub-networks)

• NetworkAnalyzer (Statistical analysis of networks)

• Agilent Literature Search (Network creation)

• CyGoose (Gaggle communication)

• See http://cytoscape.org/plugins for many moreHuman Genetics

Timeseries and BinGO: Aggregate

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Timeseries and BinGO: Analyze

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Timeseries and BinGO

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Timeseries and BinGO

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GOlorize plug-in (Pasteur)

• Node placement on the

basis of both the

connection structure

(the edges) and the

class structure (GO)

• A modification of the

classic force-directed

layout algorithm

• Beyond GO classes, other class information can be used though attributes (e.g. active modules, complexes)

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GOlorize plug-in interface

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Î“

Default settings for theclass attractive forceand separation factor

Class-directed network layout

Example: genetic interaction network

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Standard Spring-embedded layout algorithm in Cytoscape


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Spring-embedded layout algorithm with GO colour-coding


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Final results of the GOlorize layout algorithm in Cytoscape

Garcia et al. Bioinformatics 2007

Find Network Clusters - MCODE Plugin

• Network clusters are highly interconnected sub-networks that may be also partly overlapping

• Clusters in a protein-protein interaction network have been shown to represent protein complexes and parts of biological pathways

• Clusters in a protein similarity network represent protein families

• Network clustering is available through the MCODE Cytoscape plugin

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Network Clustering7000 Yeast interactionsamong 3000 proteins

Human GeneticsBader & Hogue, BMC Bioinformatics 2003 4(1):2

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Proteasome 26S

Proteasome 20S

Ribosome

RNA Pol core

RNA Splicing

Bader & Hogue, BMC Bioinformatics 2003 4(1):2

Find Network Motifs - Netmatch plugin

• Network motif is a sub-network that occurs significantly more often than by chance alone

• Input: query and target networks, optional node/edge labels

• Output: topological query matches as subgraphs of target network

• Supports: subgraph matching, node/edge labels, label wildcards, approximate paths

• http://alpha.dmi.unict.it/~ctnyu/netmatch.htmlHuman Genetics

Finding query sub-networks

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Query Results

Ferro et al. Bioinformatics 2007

Finding Signaling Pathways• Potential signaling pathways from plasma membrane to nucleus via cytoplasm

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Raf-1

MekMAPK

TFs

Nucleus - Growth ControlMitogenesis

MAP Kinase Cascade

Ras

NetMatch query

Shortest path betweensubgraph matches

Signaling pathway exampleNetMatch Results

Find Active Subnetworks• Active modules are sub-networks that show

differential expression over user-specified conditions or time-pointsMicroarray gene-expression attributesMass-spectrometry protein abundance

• MethodCalculate z-score/node, ZA score/subgraph,

correct for random expression data samplingScore over multiple experimental conditionsSimulated annealing-based search method is

used to find the high scoring networksHuman Genetics

Ideker T, Ozier O, Schwikowski B, Siegel AF Bioinformatics. 2002;18 Suppl 1:S233-40

Finding active modules

Human GeneticsIdeker T et al. Science 2001; Bioinformatics 2002

jActiveModules plug-in

Input: interaction network and p-values for gene expression values over several conditions

Output: significant sub-networks that show differential expression over one or several conditions

Cerebral: Cellular location and expression data

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Concluding

• Cytoscape is a proven valuable tool for integrative bioinformatics

• Easily extensible: well suited to answer new biological research questions

• Analyses can be tedious for biologists; up to bioinformaticians to translate these in simple workflows

• Therefore: bioinformaticians, integrate into wet-lab research groups!

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Some notes…

• Plugin lifetime– Maintenance– Interoperability

• Visualization issues…– Standard biologist layouts– Fancy visuals

Cytoscape 3.0 aims to solve these issues (amongst others)

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Availability

• Cytoscape:– http://cytoscape.org– [email protected] – [email protected]

• R2– Available shortly through http://humangenetics-amc.nl– Keep yourself posted on

http://groups.google.com/group/r2-announce

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Documents

cytoscape