Agilent Confidential

Anya Tsalenko

Allan Kuchinsky

Agilent Laboratories

July 13, 2012

Enrichment Network Analysis

and Visualization (ENViz)

global program that offers

student developers stipends to

write code for various open

source projects.

Agenda

• Introduction to integrative analysis

• Cytoscape at a glance

• ENViz walkthrough

• Next steps

Genomic Workbench

Integrated Analysis Network Biology

Integrated Biology

Informatics

Primary Analysis

LC/MS

GC/MS

Microarrays

Target Enrichment

NMR

Microfluidics

Proteins

Metabolites

DNA / RNA

miRNA

GeneSpring MassHunter

Workstation

Genome Browser

Public Data

Hypothesis, experiment, model

Integrative Biology

Example: breast cancer study

“miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors”, 2011

• Cancer dataset from Anne-Lise

Børresen-Dale Lab in Norwegian Radium Hospital, Oslo

• 100 breast tumor samples with various characteristics

• Matched miRNA and mRNA data, Agilent microarrays

Correlation of miRNA and mRNA expression,

miR-150

Sorted expression of miRNA -150

Genes most correlated to miR-150

across 100 breast cancer samples

Enrichment analysis of genes correlated to

miR-150

GO terms enrichment analysis in the top of the list of genes ordered by

correlation to miR-150 based on minimum Hypergeometric Statistics

(Eden et al, PLoS CB 2007)

mHG p-value<E-147

Biological validation

Association between miR-19a

and the cell-cycle module was

substantiated as an association

to proliferation.

Further validated using high-

throughput transfection assays

where transfection of miR-19a

to MCF7 cell lines resulted in

increased proliferation.

GO enrichment

for genes

correlated to

miR-19a

Annotation

Generic 3 matrices enrichment software Two different types of measurements in the same set of samples:

mRNA and miRNA expression (or other non-coding RNAs)

mRNA expression and quantitative clinical phenotype

mRNA expression and metabolites levels

mRNA expression and copy number

Analysis is based on statistical enrichment in lists ranked by correlation

Enrichment can be calculated based on any other annotation such as GO, pathway or disease ontology

Roy Navon

Agenda

• Introduction to integrative analysis

• Cytoscape at a glance

• ENViz walkthrough

• Next steps

http://www.cytoscape.org

OPEN SOURCE Java platform for integration of systems biology data

• Layout and query of networks (physical, genetic, social, functional)

• Visual and programmatic integration of network state data (attributes)

• Ultimate goal: provide tools to facilitate all aspects of network assembly, annotation, and use in biomedicine.

Shannon et al. Genome Research 2003 Cline et al. Nature Protocols 2007

Downloaded approximately 3000 times per month, ~137 plugins (1st June 2011)

Cytoscape at a glance

Genetic and protein

interaction networks

Subnetworks active in

disease

Host pathogen

interactions

Functional enrichment

maps

Linked structural,

networked data

Genetic regulatory networks Signaling, metabolic pathways

Agenda

• Introduction to integrative analysis

• Cytoscape at a glance

• ENViz walkthrough

• Next steps

ENViz: what it is Enrichment Network Visualization (ENViz): a Cytoscape plugin

for integrative statistical analysis and visualization of multiple sample matched

data sets

Use the main control panel to:

• Specify input primary data, pivot, and

annotation files

• Run analysis

• Set thresholds that control the size of

the enrichment network to visualize

• Run the visualization

Separate sub-panels can be collapsed or

expanded by clicking on their handles

(collapsible subpanels, Bader Lab, U

Toronto)

Interactive Legend:

• graphical overview of the workflow.

• click on labeled boxes for file prompt.

• drag and drop a file reference onto a

labeled box.

Control Panel

Enrichment Network

• Example of enrichment network built from mRNA and miRNA data from Enerly et al, using

WikiPathway annotation.

• Results are represented as bi-partite graph: nodes = pathways (green) and miRNAs (grey).

• Edge (i,j) represents enrichment of pathway j in the set of genes whose expression correlate the

expression pattern of miRNA i. red = positive correlation, blue = negative correlation

• Double-click on edge to load its pathway into Cytoscape.

Enrichment Network Zoom:

• Zoom in to see details around selected nodes and edges

• See zoomed-in network in the context of the whole network on the bottom left

Pathway visualization in WikiPathways

• Click on selected edge shows corresponding WikiPathway

• All gene nodes in the mRNA processing pathway that map to primary data

elements are color coded (blue -> red) for correlation score between the primary

data element (mRNA) and the pivot data element for the clicked edge (hsa-miR-

92a) • thick borders and high opacity those genes above

correlation threshold that were included in the gene set

used for enrichment analysis.

Tiling Pathway views

• Double-click on a pathway Node to loads multiple WikiPathways, each one colored by correlation

with the specific pivot datum for an Edge, connected to the Node, up to a user-configurable limit

• Network views are tiled in a ‘small multiples’ view that accentuates contrasts between correlations

for different pivot data.

Gene Ontology visualization

• enrichment networks built from Enerly et al. mRNA and miRNA data and Gene Ontology

annotation.

• left = bi-partite graph for GO terms (yellow -> red scale) and miRNA (grey)

• edge (i,j) is enrichment of GO term j in in the set of genes that correlate with miRNA i.

• right = GO summary network for GO terms in the left enrichment network. Each GO nodes

color-coded (yellow to red) by maximum enrichment score for its set of pivot nodes.

• parent terms are added, to complete the GO hierarchy view.

miR-150 - oriented GO Terms

• Double-click on an pivot node in the enrichment network to show GO terms in the GO Summary

network that have significant enrichment values for the pivot datum.

• Enrichments for GO terms and genes correlated to miR-150 are color-coded yellow -> red.

Agenda

• Introduction to integrative analysis

• Cytoscape at a glance

• ENViz walkthrough

• Next steps

Next steps

• Working on performance, completeness, robustness

• Extend support for other organisms beyond Homo sapiens, Mus

Musculus, mycobacterium tuberculosis

• Extend the range of database id mappings

• beta-release tentatively planned for end of Summer 2012

• Possible future features: heatmap view, sample grouping, more annotation

types (TFs, disease ontologies), crosstalk visualization

Acknowledgements

• Agilent Technologies

– Roy Navon, Zohar Yakhini, Michael Creech

• Technion

– Israel Steinfeld

• Collaborators

– Norwegian Radium Hospital, Oslo: Espen Enerly, Kristine Kleivi, Vessela N. Kristensen, Anne-Lise Børresen-Dale

– UCSF/Gladstone: Alex Pico, Nathan Salomonis, Kristina Hanspers, Bruce Conklin, Scooter Morris

– Maastricht University: Thomas Kelder, Martijn van Iersel, Chris Evelo

– Cytoscape core developers and PIs: Trey Ideker, Chris Sander, Gary Bader, Benno Schwikowski, Mike Smoot, Peng Liang, Kei Ono, Leroy Hood, Ben Gross, Ethan Cerami