Transcript Immune Cell Ontology for Networks

Immune Cell Ontology for
Networks (ICON)
Immunology Ontologies and Their
Applications in Processing Clinical Data
June 11-13, Buffalo, NY
Confessions
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I am an ontological newbie
Idea for a new ontology of immune networks
Immunologists I’ve talked to like the idea
Biostatisticians I’ve talked to like the idea
So, possibly not entirely stupid
Looking for feedback and advice
Looking for friendly collaborators
Immunological case-control studies
Normal Donor
Lupus Patient
Typical case-control study
• Data collection
– Hundreds of cell subsets from flow cytometry
– Dozens of cytokines from Luminex
– Other assays (IHC, single cell PCR etc)
• Data analysis
– Pairwise comparisons
• Apply Bonferroni correction gives p >> 0.05
– Statistical aggregation e.g. PCA
• Often difficult to give biological interpretation
HOW DOES AN IMMUNE RESPONSE
ACTUALLY WORK?
Cancer microenvironment network
Missing biological knowledge
• Immune response does not consist of isolated
cells and cytokines acting independently
• Networks coordinated by cell-cell
communication
• Gap – immune network ontology
• Applied ontology that draws strength from
pre-existing ontologies
Is a network ontology feasible?
• Analysis of regulatory networks suggest that
networks map to dynamical attractors
• Typically surprisingly few attractors given
potential combinatorial explosion
• Examples
– Boolean regulatory networks (e.g. Kauffman)
– Recurring gene network motifs (e.g. Alon)
What’s needed?
• Networks consist of cells that communicate
via contact- and cytokine-mediated signaling
– Components
• Cells, cell surface molecules, cytokines
• Cell-cell interactions may be specific to
particular species, local environments and
disease states
– Contexts
• Species, tissue, disease
Gene Ontology
cell surface molecule
Protein Ontology
cytokine
Cell Ontology
cell
Components
network
Contexts
tissue
FMA
disease
Disease Ontology
species
NCBI Taxonomy
Tentative construction strategy
• Iterate
– Build cheap “weak links” graph database by text mining
• Edges for cell:cell surface molecule, cell surface molecule:cell
surface molecule, cell:cytokine, cytokine:cell surface molecule
• Question: Does text mining work for anyone here?
– Human review to identify spurious links and add species,
disease and tissue contexts
– Use “confirmed” and “spurious” links as training, validation
and test data sets to improve text mining
• Split into networks
– Split into discrete subgraphs by cutting “weakest” links
based on some method of assigning weights to edges
Usage
• Queries
– Find networks associated with a disease
– Find cell subsets, receptors and cytokines associated with a
network
– Find reagents associated with cell subsets, receptors and
cytokines
– Find networks most relevant for given cell subsets,
receptors and cytokines
• Applications
– Reference, targeted assay development, better informed
fishing expeditions
– Basic science – validate novel links or networks
App: Web accessible reference
• No existing database
• Literature review is laborious
• Useful public resource
App: Targeted assay development
• What networks are potentially active in
disease X?
• Which are the most informative cell subsets
and/or cytokines for these networks?
• What reagents are available to identify the cell
subsets and/or cytokines of interest? (Needs
additional reagent database)
App: Better fishing expeditions
• Sets of cells +/- cytokines in networks
• Test for enrichment of networks in treatment
groups rather than pairwise-comparisons
• Adapt statistical methods developed for
enrichment analysis in expression array data
(e.g. TANGO or GSEA)
• Allows integration of immune biomarkers over
multiple panels (e.g. T, B, innate flow panels,
Luminex, immunohistochemistry)
STARTING POINT
T cell social network analysis
Initial networks in Protégé
(courtesy of Anna Maria)
Acknowledgements
• Duke Center for Computational Immunology
– Tom Kepler
– Lindsay Cowell
– Anna Maria Masci
• Duke Immune Profiling Cores
– Kent Weinhold
– David Murdoch
– Janet Staats
– Sarah Sparks