CYTO U Upcoming Webinars
Collapse Cytometry Part A Spotlight: DAFi - Directed Automated Filtering and Identification of Cell Populations from Polychromatic Flow Cytometry Data

DAFi - Directed Automated Filtering and Identification of Cell Populations from Polychromatic Flow Cytometry Data

Part of the Cytometry Part A Spotlight Series

Thursday, October 25th at 2pm Eastern (US & Canada)

Presented by:

Yu “Max” Qian, PhD
Assistant Professor
J. Craig Venter Institute

Moderated by Ryan Brinkman
BC Cancer Research Center

About the Presenter

Dr. Yu “Max” Qian is an Assistant Professor of Informatics at the J. Craig Venter Institute (JCVI). Max was one of the original developers of the flow cytometry (FCM) component of ImmPort, the NIAID/DAIT-funded immunology database and analysis portal, where he developed the FLOCK clustering method for computational identification of cell populations from FCM data. He led or collaborated with other FCM bioinformatics researchers in development of data transformation methods, information standards, data models, and software systems, including FCSTrans, MIFlowCyt, FuGEFlow, and GenePattern FCM suite. Collaborating with researchers from several institutions, he has recently focused on design and implementation of a web-based computational infrastructure – FlowGate (flowgate.jcvi.org) – for supporting clinical and translational research through data-driven reproducible analysis of FCM experiment data. He has been customizing data analytical pipelines and performing computational analytics of FCM data for multiple NIH-funded research projects, including the Respiratory Pathogens Research Center (RPRC) at University of Rochester and the Human Immunology Project Consortium (HIPC) center at the La Jolla Institute for Allery and Immunology.

Webinar Summary

Although auto-gating approaches have advantages over traditional manual gating analysis, there exist roadblocks before a cytometry lab can adopt an auto-gating approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these roadblocks. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the results of DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

Learning Objectives

  1. Gain an understanding of what the cutting-edge auto-gating approaches can do and their limitations in general.
  2. Learn how DAFi works, what it can do and cannot do, as well as how to apply DAFi to the analysis of polychromatic FCM datasets.
  3. How to assess the performance of an auto-gating approach using visualization and other computational methods.
  4. Get to know the FlowGate cyberinfrastructure being developed. 

Who Should Attend

The target audience is everyone who is interested in FCM bioinformatics, especially those who have been planning to apply auto-gating approaches for computational identification of cell populations from polychromatic FCM data.

 

Formats Available: Streaming, Live Webcast + Streaming
Original Seminar Date: October 25, 2018

Approved Credit:
  • ASCP: 1 hour CMLE

  • Topics & Pricing InformationTopics & Pricing Information Cytometry Part A Spotlight: DAFi - Directed Automated Filtering and Identification of Cell Populations from Polychromatic Flow Cytometry Data
    Collapse Validation, the Key to Translatable Flow Cytometry. Part 3: Instrument Qualification

    Validation, the Key to Translatable Flow Cytometry

    Part 3

    Instrument Qualification

    NEW DATE & TIME

    Monday, October 29 at 12pm Eastern (U.S. & Canada)

    Presented by:

    Cherie Green
    Senior Scientific Manager, Flow Cytometry Biomarkers
    Genentech
    San Francisco, CA

    About the Presenter

    Cherie has been a long standing member of the cytometry community for over 20 years working in hematopathology and biotech laboratories.  She is passionate about development of robust biomarker assays to support all phases of drug development.  Currently, she oversees the Flow Cytometry Biomarker group in the Development Sciences department at Genentech, a member of Roche Group in San Francisco, CA. Her group is responsible for the development and validation of clinical biomarker assays in the areas of infectious, autoimmune, and oncology diseases.  She has served as the co-chair of Flow Cytometry Action Committee of the American Association of Pharmaceutical Scientists (AAPS) and has co-authored many consensus/recommendation papers on topics specific to drug development such as instrument and assay validation, sample stability, and receptor occupancy.  

    Webinar Summary

    The foundation of good data starts with the instrument. While substantial effort is often invested in development and validation of analytical methods or analysis, instrument validation is often neglected.  It is essential to apply the same analytical and scientific rigor to the platform generating the data.  From initial optimization and characterization of performance to establishing QC systems to ensure longitudinal data comparability, instrument validation strategies are critical components of generating robust and reliable data.  This is true for all laboratory environments but particularly relevant for regulated labs providing decision-enabling biomarkers. Generating quality data plays a critical role in bringing new therapeutic options to the medical community; drugs which eventually manifest as successful new treatments for those individuals afflicted with disease.

    Learning Objectives

    In this webinar, you will learn the basic principles of instrument validation. Validation of flow cytometers used in regulated environments provides assurance that the output generated on these instruments is reproducible and precise.  The most relevant elements of instrument validation include testing to verify that an instrument: i) is installed properly and ii) performs as intended.  This includes establishing controlled procedures for installation, maintenance, calibration, cross-instrument standardization, and longitudinal performance monitoring.  This course will review basic concepts of instrument validation and provide examples of each step in the process that can be applied in your lab.

    Who Should Attend

    Anyone interested in getting robust and reliable flow cytometry data.

     

     

     

    Formats Available: Streaming, Live Webcast + Streaming
    Original Seminar Date: October 29, 2018
    On-Demand Release Date: Available Now

    Approved Credit:
  • ASCP: 1 hour CMLE

  • Topics & Pricing InformationTopics & Pricing Information Validation, the Key to Translatable Flow Cytometry. Part 3: Instrument Qualification
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