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




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

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NetBioSIG2012 anyatsalenko-en-viz

  • 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