Flow Cytometry

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Flow Cytometry FuGO

• Ryan BrinkmanTerry Fox Laboratory
• BC Cancer Research CenterDept. of Medical
  Genetics, University of British Columbia

2
Why?

• A. SPECIFIC AIMS
• Establish criteria to record FCM experiments and
  develop standards to describe them
  unambiguously.
• Generate the Fluorescence Activated Cell
  Experiment Ontology (FACEOntology) to facilitate
  data mining by enabling structured queries of the
  elements of experiments through the use of a
  controlled vocabulary.

3
Who?

• Michael Ochs
• Robert Gentleman
• Clayton Smith
• Perry Haaland
• Adam Treister
• ISAC
• IEEE PAR1953.2
• NIH (EB5034-01)

4
Goal

• To facilitate the interchange of flow cytometric
  analyses between software packages, investigators
  and laboratories
• Allowing people to do stupid things in a standard
  way

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Why flow cytometry standards?

• Increasing data throughput
• 1,000 samples 6 parameters / day
• Data (information) processing has to increase
• Slow
• Error prone
• Not standardized
• Most limiting aspect of technology

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How?
CollaborativeWeb Space
Compendium
Translation
Graphs
Statistics
Visualization
Java
R
DescriptiveVocabulary
File Formats
Database Schema
Object Model
Experimental Description
Gating Specification
XML
SQL
UML
OWL
www.flowcyt.org
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Contribution
8
Challenges

• The flow cytometry community
• If we build it, will they come?
• High throughput flow is new
• Lack of ontology experience
• What's in it for me?
• The people building the ontology are idiots

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Whats in it for us?

• Why re-invent the ontology?
• It seems many terms will overlap between groups
• BioMaterial, Protocol, Experiment
• Concentrate on application
• Controlled vocabulary for database
• flowLIMS, instrument registry, centralized
  repository
• Guide for developing manuscript preparation
  guidelines (e.g., MIFACE)