Representing Flow Cytometry Experiments within FuGE

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Representing Flow Cytometry Experiments within FuGE Josef Spidlen 1 , Peter Wilkinson 2 , and Ryan Brinkman 1 1 BC Cancer Research Centre, Vancouver, BC, Canada 2 University of Montreal, Montreal, QC, Canada

description

Representing Flow Cytometry Experiments within FuGE. Josef Spidlen 1 , Peter Wilkinson 2 , and Ryan Brinkman 1 1 BC Cancer Research Centre, Vancouver, BC, Canada 2 University of Montreal, Montreal, QC, Canada. Reagent. Reporter (e.g., PE). Detector (e.g., Anti-CD4). - PowerPoint PPT Presentation

Transcript of Representing Flow Cytometry Experiments within FuGE

Page 1: Representing Flow Cytometry Experiments within  FuGE

Representing Flow Cytometry Experiments within FuGE

Josef Spidlen1, Peter Wilkinson2, and Ryan Brinkman1

1BC Cancer Research Centre, Vancouver, BC, Canada2University of Montreal, Montreal, QC, Canada

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How can we accurately model “complex” flow cytometry

experiments in an exact manner?

FCS parameter(e.g., FL1)

Reagent

Reporter(e.g., PE)

Detector(e.g., Anti-CD4)

Cell population(e.g., CD4+)

Filter settings?

Emission spectra?

Compensation? ?

?

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Why would we even want to?

• Sharing experimental details• Understanding third party experiments

– Collaboration– Independent validation

• Common and sharable software tools– High-throughput data processing– New data processing methods

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FuGE

• Functional Genomics Experiment Object Model– A model of the common components of functional

genomics experiments– FuGE is developed by members of MGED/PSI with

input from ‘cross-omics’ experimentalists– Aims to help the development of data standards– Should allow some cross-compatibility between

different ‘omics’ experiments

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What is FuGE?

• An object model in UML– An XML Schema (generated from UML)– A software API (created from UML)– ER schema (generated from UML)

• Milestone 3 UML2 - August 2006• Current state: Version 1.0 candidate

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Benefits of shared model components

• Queries over common annotation– Samples, hypotheses, protocols

• Shared software for experimental annotation and analysis– Reduced development and learning times through the

sharing of consistent practice– Eased integrating of functional genomics data

• Developing standards for each technique is a hard problem– Shared resources could alleviate problems

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FuGE

Common

Bio

Description

Audit

Ontology

Protocol

Reference

Investigation

Data

Material

ConceptualMolecule

Common:• General data format management• Auditing• Referencing external resources• Protocols

Bio:• Investigation structure• Data• Materials (organisms, solutions, compounds)• Theoretical molecules e.g., sequences

FuGE structure

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Using FuGE in practice

1. Extend UML with domain-specific components

• Encapsulate details in classes/attributes• Use “generic” classes with text-based

descriptions

2. Reference a FuGE entry for investigation structure and bio samples description

3. Define ontologies and use FuGE as it is for experimental metadata

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FuGE extensions• MAGE V2

– Format for microarray data and annotations• GelML

– Gel electrophoresis, format for methods and results• spML

– Sample processing: liquid chromatography, capillary electrophoresis, …• CPAS

– Computational Proteomics Analysis System – set of bioinformatics tools to help scientists store, analyze, and share data from experiments and clinical trials

• PRIDE – Proteomics Identification Database contemplating FuGE for data format

• Metabolomics community – considering• MIACA (Minimum Information About a Cellular Assay) – considering

• Flow Cytometry– FuGE was chosen as core for flow cytometry object model during

FICCS OMWG Development Workshop (Dallas, October 2006)

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FuGE – Main Abstract Classes

• Everything is “Describable”– Text based description– Ontology reference– Custom properties (keyword / value pairs)

• Most classes are “Identifiable”– “Identifiable” is “Describable”– Unique identifier– Name, database references

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FuGE Protocol Types

1. Material treatment:Flow sample preparation

2. Data acquisition:Cytometer generates FCS

3. Data and material acquisition:Flow sorting

4. Data transformation:Compensation, gating, scaling, visualization

MaterialMaterial

DataMaterial

DataData

DataMaterial

Material

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Flow Cytometry – DataFuGEFlow

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Flow Cytometry – MaterialFuGEFlow

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Flow Cytometry – ProtocolFuGEFlow

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Computational ProtocolFuGEFlow

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Computational ProtocolFuGEFlow

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Computational ProtocolFuGEFlow

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FuGEFlow

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Conclusions

• Initial work on extending FuGE has been done– Can be downloaded using subversion from

https://svn.sourceforge.net/svnroot/flowcyt/ – Pretty high level so far

• Need to incorporate more details• Need to validate the model

– Encoding various use cases– An iterative approach needed

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Acknowledgement

• Members of the FICCS OMWG– Keith Boyce, Ryan Brinkman, Jennifer Cai, Mark

Dalphin, Megan Kong, Jamie Lee, Yu (Max) Qian, Richard Scheuermann, Peter Wilkinson, and others.

• Introduction to FuGE based on original presentations from FuGE development team – Angel Pizarro, Andrew Jones, Paul Spellman, Michael

Miller, and others.