Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs. Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform. Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).

1. Enrichment Network Analysis
and Visualization (ENViz)
global program that offers
student developers stipends to
Anya Tsalenko write code for various open
Allan Kuchinsky source projects.
Agilent Laboratories
July 13, 2012
Agilent Confidential

2. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps

3. Integrative Biology
Primary Analysis
NMR
Proteins
Genomic Workbench GeneSpring MassHunter
Workstation Public Data
LC/MS Integrated Biology
Metabolites GC/MS Informatics
Microarrays
DNA / RNA Target Enrichment
Network Biology Integrated Analysis Genome Browser
miRNA
Microfluidics
Hypothesis, experiment, model

4. 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

5. Correlation of miRNA and mRNA expression,
miR-150
Sorted expression of miRNA -150
Genes most correlated to miR-150
across 100 breast cancer samples

6. Enrichment analysis of genes correlated to
miR-150
mHG p-value<E-147
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)

7. Biological validation
GO enrichment
Association between miR-19a for genes
and the cell-cycle module was correlated to
substantiated as an association miR-19a
to proliferation.
Further validated using high-
throughput transfection assays
where transfection of miR-19a
to MCF7 cell lines resulted in
increased proliferation.

8. Generic 3 matrices enrichment software
Two different types of
measurements in the same set of
samples:
 mRNA and miRNA expression (or
Annotation
other non-coding RNAs)
Roy Navon
 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

9. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps

10. Cytoscape at a glance Shannon et al. Genome Research 2003
Cline et al. Nature Protocols 2007
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.
Downloaded approximately 3000 times per month, ~137
plugins (1st June 2011)
Signaling, metabolic pathways Genetic regulatory networks
http://www.cytoscape.org
Host pathogen Functional enrichment Linked structural,
Genetic and protein Subnetworks active in maps networked data
interactions
interaction networks disease

11. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps

12. ENViz: what it is
Enrichment Network Visualization (ENViz): a Cytoscape plugin
for integrative statistical analysis and visualization of multiple sample matched
data sets

13. Control Panel
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.

14. 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.

15. 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

16. 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.

17. 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.

18. 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.

19. 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.

20. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps

21. 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

22. 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