1. Enrichment Network Analysis and Visualization (ENViz) Cytoscape plugin for integrative statistical analysis and visualization of multiple sample matched data sets Anya Tsalenko Agilent Laboratories December 14, 2012

2. Why ENViz? Many high throughput data sets measured in the same set of samples: - ‘omics’ - proteomics - metabolomics Rich databases with systematic annotations: - GO - pathways - drug targets How do we analyze this data together to get deeper biological insights into studied phenotype?

3. Genomic Workbench Integrated AnalysisNetwork 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 Integrated Biology

4. Example: breast cancer study “miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors” Enerly et al, PLoS One 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 sorted by correlation to miR-150 across 100 breast cancer samples

6. Enrichment analysis of ranked list 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 Analysis and visualization in GOrilla software http://cbl-gorilla.cs.technion.ac.il/

7. 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

8. Generic 3 matrices enrichment analysis 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 phenotypes  mRNA expression and metabolites levels  mRNA expression and copy number Roy Navon Enrichments Correlations Analysis is based on statistical enrichment of annotation elements in lists ranked by correlation Enrichment can be calculated based on any annotation such as GO, pathway, disease ontology or other custom primary data categories Primary Data genes samples Pivot Data samples miRNAs/other Annotation Pathways/GO/other

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

10. Use the main control panel to: 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

11. Enrichment Network Enrichment network built from mRNA and miRNA data from Enerly et al, using WikiPathway annotation. Results are represented as bi-partite graph: nodes = pathways (yellow->red) and miRNAs (grey). Edge represents enrichment of pathway node in the set of genes whose expression correlate the expression pattern of miRNA node, red = positive correlation, blue = negative correlation

12. 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

13. Pathway visualization in WikiPathways Click on selected edge loads and 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 show genes above correlation threshold that were included in the gene set used for enrichment analysis.

14. 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.

15. Gene Ontology enrichment and visualization Enrichment network 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 is enrichment of the GO term in the set of genes most correlated with the miRNA. right = GO summary network for GO terms in the left enrichment network. Each GO nodes color-coded by cumulative enrichment score for its set of pivot nodes. parent terms are added, to complete the GO hierarchy view.

16. 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 selected pivot. GO Summary network on the right is color-coded by enrichment of genes correlated to miR-150

17. Summary: key features of ENViz Enrichment of annotation elements among primary data most correlated to secondary(pivot) data across a set of samples for each pivot and each annotation node Representation of results as bi-partite graph (network) Pathway and GO enrichment analysis with customized visualization Zoom-in into results in the context of WikiPathways Interactive and intuitive data loading and analysis Power of network analysis in Cytoscape

18. Next steps Beta-release for collaborators email: anya_tsalenko@agilent.com Working on performance, completeness, robustness for Cytoscape plugin release Extend support for other organisms beyond Homo sapiens, Mus Musculus, mycobacterium tuberculosis Extend the range of database id mappings Possible future features: heatmap view, sample grouping, more built-in annotation types (TFs, disease ontologies)

19. Acknowledgements Agilent Team –Allan Kuchinsky, 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