Computational Population Discovery

Computational Population Discovery and Sorting for Functional and Multi-Omics Analysis.

As the number of dimensions being interrogated rises, thorough identification and characterization of populations using traditional manual gating analysis becomes more challenging. Manual gating requires subjective selection of features and declaration of population membership using 1 or 2 dimensional plots, an approach that not only requires previous knowledge of the phenotypic expression patterns of populations being identified, but also invariably applies operator bias when creating and drawing gates. Computational methods for clustering attempt to overcome these limitations by classifying events into populations defined by all dimensions simultaneously as a region of local density within marker space, acting more objectively, and potentially identifying novel populations that would be ignored or missed with manual gating. But how to get started employing such methods? Which clustering and dimensionality reduction algorithms are the best ones to use? How do you compare many samples, identify cluster phenotypes and visualize the populations resulting from a clustering algorithm? And perhaps most important, how can you sort an algorithm-derived population for downstream functional and single cell multi-omics studies? Join us as we address these questions and discuss how to employ the newest suite of tools in FlowJo, which enable computational discovery and sorting.

Timothy Quinn Crawford, PhD recorded this presentation during CYTO Virtual 2020