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FCM Data Management and Analysis in ImmPort

FCM Data Management and Analysis in ImmPort. Richard H. Scheuermann, Ph.D. Department of Pathology and Division of Biomedical Informatics U.T. Southwestern Medical Center, Dallas, TX. Outline. The Immunology Database and Analysis Portal – ImmPort Data management challenges

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FCM Data Management and Analysis in ImmPort

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  1. FCM Data Management and Analysis in ImmPort Richard H. Scheuermann, Ph.D. Department of Pathology and Division of Biomedical Informatics U.T. Southwestern Medical Center, Dallas, TX

  2. Outline • The Immunology Database and Analysis Portal – ImmPort • Data management challenges • Support for large projects using diverse experiment methodologies • Extensive clinical data • Automated FCM data analysis challenges • Cross-sample comparison • Linkage of automated FCM analysis results with knowledge about cell types • Use of the Cell Ontology

  3. ImmPort Purpose and History • NIH/NIAID/DAIT would like to: • maximize the return on the public investment in basic, translational and clinical research • allow investigators to more effectively extract meaningful information from the vast amounts of data generated from advanced research technologies • => data sharing policy • Bioinformatics Integration Support Contract (BISC) to support data sharing for all DAIT-funded programs - basic, translational and clinical research • Immunology Database and Analysis Portal (ImmPort) - www.ImmPort.org • Archive and manage basic and clinical research data • Integrate these research data with extensive biological knowledge • Support analysis of these integrated data

  4. Home page www.immport.org

  5. Support for many large projects that use a variety of different experiment methodologies, including FCM Challenge 1

  6. Browse Data/ ImmPort Research Data/ ImmPort Supported Programs ImmPort Research Data | My Work Bench Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations Program Population Genetics Analysis Program: Immunity to Vaccines/Infections Program HLA Region Genetics in Immune-Mediated Diseases Program Immune Modeling Centers Other Consortium Projects

  7. Browse Data/ ImmPort Research Data/ ImmPort Supported Programs ImmPort Research Data | My Work Bench Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations Program Grants/Contracts/Projects:

  8. Extensive clinical data for correlative analysis Challenge 2

  9. AUTOMATED FCM ANALYSIS

  10. Challenge 3 • Identification of same cell populations in multiple samples

  11. Challenge 4 • Linkage of automated results with knowledge about known cell types

  12. 17 B Cell Populations in Blood A PB GSM GNSM UM3-4 UM1-2 CD38 CD27 B220 N1-3 DNM IgD CD24 IgG

  13. Population characteristics

  14. Summary Statistics

  15. B cell component of the Cell Ontology http://www.obofoundry.org/

  16. FCM Data Challenges • Data management challenges • Support for large projects using diverse experiment methodologies • Extensive clinical data for correlative analysis • Automated FCM data analysis challenges • Cross-sample comparison • Linkage of automated FCM analysis results with knowledge about cell types • Use of the Cell Ontology

  17. Acknowledgments UT Southwestern Yu (Max) Qian David Dougall Megan Kong Jamie Lee Jennifer Cai Jie Huang Nishanth Marthandan Diane Xiang Young Bun Kim Paula Guidry Eva Sadat Northrop Grumman John Campbell Carl Dahlke Yue Liu Liz Thompson Jeff Wiser Mike Attasi Immune Tolerance Network Dave Parrish Keith Boyce Tom Casale Jeff Bluestone Ignacio Sanz (Rochester) Chungwen Wei (Rochester) Tim Mosmann (Rochester) Adam Seegmiller (UTSW) Nitin Karandikar (UTSW) Christine Martens (Emory) Chris Ding (UTA) Alex Diehl (Jackson Labs) Terry Meehan (Jackson Labs) Martin Zand (Rochester) Supported by NIH N01AI40076 and N01AI40041

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