CYTO Virtual Interactive 2021 Scientific Tutorial - Computational Cytometry – a Basic Guide for Users and Resources for SRLs
Cytometry data is typically analyzed by creating manual sequential gates in two-dimensional plots to find cell populations of interest. The recent development in instrumentation and reagents has dramatically increased the number of simultaneous parameters that can be measured in a single cell, leading to massive and complex datasets and the need for faster, scalable, and less subjective analytical solutions. This transformation in how data is being analyzed requires learning new skills and unsupervised computational analysis, which can be daunting and intimidating. The goal for this tutorial is to simplify the understanding of the tools, packages, and software available for computational data analysis in the SRL context.
Sofie Van Gassen, PhD
VIB-UGent Center for Inflammation Research
Sofie Van Gassen received her PhD in computer science engineering from Ghent University in 2017. During her PhD, she developed machine learning techniques for flow cytometry data, including the FlowSOM algorithm. She is now working on tools for computational cytometry in clinical studies during her postdoc in the Saeys lab (VIB-UGent Center for Inflammation Research). She is an FWO postdoctoral fellow and an ISAC Ingram Marylou Scholar.
Diana Bonilla-Escobar, PhD
US Technical Lead for Application Support
Diana Bonilla Escobar is an immunologist with a PhD from Texas A&M University, a postdoctoral degree from Baylor College of Medicine, and an ASCP-accreditation as a cytometry specialist. She has more than 20 years of experience as a biomedical scientist for a variety of applications, including infectious diseases and cancer at MD Anderson Cancer Center. She is a former ISAC SRL Emerging Leader and highly involved in education with cytometry societies such as ISAC, FlowTex, and LatinFlow. She currently works as the US Technical Lead for Application Support at Cytek Biosciences.
CMLE Credit: 1.0