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

About the Presenter

Yu "Max" Qian, PhD
Assistant Professor of Informatics
J. Craig Venter Institute

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. It was found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these roadblocks. This new approach is named 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

• Gain an understanding of what the cutting-edge auto-gating approaches can do and their limitations in general.
• 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.
• Assess the performance of an auto-gating approach using visualization and other computational methods.
• Get to know the FlowGate cyberinfrastructure being developed.

Who Should Attend

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.

CMLE Credit: 1.0