Transcript ppt - National Resource for Network Biology

Reactome Functional Interaction Network
Cytoscape Plugin
Robin Haw
15th December 2012
9th Annual Cytoscape Workshop
www.reactome.org
Ministry of Economic
Development and
Innovation
Network Module Based Analysis of Disease Datasets
• No single mutated gene is necessary and
sufficient to cause cancer.
– Typically one or two common mutations
(e.g. TP53) plus rare mutations.
• Analyzing mutated genes in a network
context:
– reveals relationships among these
genes.
– can elucidate mechanism of action of
drivers.
– facilitates hypothesis generation on
roles of these genes in disease
phenotype.
• Network analysis reduces hundreds of
mutated genes to < dozen mutated
pathways.
What is a Functional Interaction Network?
• A high coverage, reliable interaction network based on manually curated
pathways extended with predicted interactions.
• The plugin is a resource for the constructing FI sub-networks based on
gene lists.
• Tools that:
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Provide the underlying evidence for FIs.
Identify network modules of highly-interacting groups.
Perform functional enrichment to annotate modules.
Display source pathway diagrams and overlay with a variety of information
sources such as cancer gene index annotations.
• Method and practical application: A human functional protein interaction
network and its application to cancer data analysis, Wu et al. 2010 Genome
Biology.
http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin
Construction of the Reactome FI Network
Curated data sets
Pairwise data sets
human PPI
PPI inferred from fly,
worm & yeast
PPI from text mining
Gene co-expression
GO annotation on
biological processes
ENCODE interactions
Protein domain-domain interactions
Naïve Bayes Classifier
Annotated Functional
Interactions
Predicted Functional
Interactions
Reactome FI Network: 273K interactions and 11K proteins
FI Network Analysis Pipeline
Your gene list (e.g. mutated, over-expressed, down-regulated,
amplified or deleted genes in disease samples)
Project genes of interest onto Reactome F.I. Network
Identify Disease/Cancer Subnetwork
Apply Clustering Algorithms
Apply Pathway/GO Annotation to each cluster
Perform Survival Analysis (optional)
Generate Biological Hypothesis!
Predict Disease Gene Function
Classify Patients & Samples
Software Architecture – Reactome FI plugin
Database in
MySQL
hibernate
Reactome API
Server Side in
Spring
Container
XML
Messaging
RESTful WS
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FIs and Pathways
Cancer Gene
Index
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Bridge to fetch FIs and
Pathways
Network clustering:
spectral and edgebetweenness
Pathway and GO term
enrichment analysis
Cancer gene
annotations: caBIG
cancer gene index
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Cytoscape
Network module display:
color genes based on
network modules
Pathway/GO enrichment
result display: table view
FI annotations: directions,
and scores
View of cancer gene index
annotation
Pathway diagram view:
highlight genes in pathway
diagrams
File Formats
• Choose Plugins, Reactome FIs.
• FI plug-in supports four file formats:
Microarray
Simple
Gene/Sample
NCI MAF
Gene
(mutation
(array)
List
Number
data
annotation
file
Pairs file)
MSI2
PTPRT
PELO
SLC18A1
TACC2
FAM148B
PRC1
MSTN
ATP6V1G2
APOE
IMPA2
AGER
XPO5
MEST
RREB1
BAT1
WIPI1
CATSPERB
SSR1
VEGFA
Reactome Functional Interactions
• Three edge attributes are created:
– FI Annotation.
– FI Direction.
– FI Score (for predicted FI).
• Edges display direction attribute values.
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‘>’ for activating/catalyzing.
‘|’ for inhibition.
solid line for complexes or inputs.
--- for predicted FIs.
• Additional features
– Query FI Source.
– Fetch FIs for particular node.
Cluster FI network
• Plugin runs spectral partition based network clustering (Newman, 2006) on
the displayed FI network.
• MCL graph clustering algorithm is used with the gene expression data.
• After network clustering, nodes in different network modules will be shown
in different colours (max 15 colours).
Analyze module functions
• Pathway or GO term enrichment analysis on individual network
modules.
– Use filter to remove small network modules.
– Filter by FDR.
• Select nodes in the network highlights the corresponding gene
sets
• Select rows in Data panel highlights contributing genes in
network.
Overlay Cancer Gene Index
• Load the NCI disease terms hierarchy in the Control
panel.
– Select a disease term in the tree to select all nodes that have this
annotation or one of its sub-terms.
• View the NCI gene annotations for an individual node.
Module Based Survival Analysis
• Discover Prognostic Signatures in Disease Module datasets.
• Requires appropriate clinical data file.
• Based on a server-side R script that runs either CoxPH or
Kaplan-Meyer survival analysis.
Example: Analysis of Cancer Genome
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HGS OvCa Exome Sequencing data.
316 Patient Samples.
MAF contains 8420 NS mutations.
Survival data.
Publication: TCGA Consortium, Nature 2011.
HGS OvCa-Reactome FI Network
Find and Annotate Network Modules
Module 3:
Rho GTPase
signaling
Module 0:
DNA Repair, TP53
signaling, Cell Cycle
Regulation
Module 6:
Calcium
signaling
Module 8:
PI3K
signaling &
metabolism
Module 7:
Cell cycle
checkpoints
Module 1:
Insulin & ErbB
signaling
Module 2:
Integrin
signaling
Module 5:
Wnt & cadherin
signaling
Module
4:
GPCR
signaling
Module Based Survival Analysis
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Discovering Prognostic Signatures in Cancer Module
Datasets
FI plugin performs CoxPH and Kaplan-Meyer survival analysis if clinical data is
available for the samples used in the network construction.
Module 6
Module 6
Calcium
signaling
Patient
samples with
mutated
Module 6
genes
HGS OvCa Module Map
CoxPH
Kaplan-Meyer
Future Work and Conclusions
• Increase the size and functionality of the Reactome Fl
network.
• add additional sources of functional interactions and annotations.
• employ other clustering algorithms.
• Cytoscape FI network plugin provides a powerful way to
analyze cancer and disease datasets
• lets anyone perform the workflow of discovering and annotating
network modules.
• reveals functional relationships amongst cancer/disease genes.
• to identify cancer prognostic signatures to predict patient survival.
Acknowledgements
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Guanming Wu
Adrian Duong
Marija Orlic-Milacic
Karen Rothfels
Lisa Matthews
Marc Gillespie
Irina Kalatskaya
Christina Yung
Michael Caudy
David Croft
Phani Garapati
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Bijay Jassal
Steve Jupe
Bruce May
Antonio Fabregat Mundo
Veronica Shamovsky
Heeyeon Song
Joel Weiser
Mark Williams
Henning Hermjakob
Peter D’Eustachio
Lincoln Stein
Ministry of Economic
Development and
Innovation