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Identifying differential cell populations in flow cytometry data accounting for marker frequency

By Alice Yue, Cedric Chauve, Maxwell W. Libbrecht, Ryan Brinkman

Posted 16 Nov 2019
bioRxiv DOI: 10.1101/837765

Bio-markers are measurable indicators that predict a given phenotype or disease. We introduce a method capable of discovering biologically meaningful, interpretable, and actionable differential cell population bio-markers from flow cytometry samples of different phenotypes. Cell populations are groups of cells that contain the same set of proteins. Differential cell populations are those that have a significantly changed abundance between samples of different phenotypic types. Existing methods for differential cell population identification fall into one of three categories: methods that 1) compare a limited set of pre-specified mutually exclusive cell populations that do not share cells, 2) find differential cell populations as a byproduct of another procedure, and 3) compare overlapping cell populations in a search space of all possible cell populations. The cell populations analyzed in 3) are dependent on each other and can be difficult to interpret. For example, an increase in one cell population (e.g. a bio-marker for a phenotype of interest) may induce an increase in several cell populations that share its cells. Our method solves this issue by taking into account these dependencies by finding only cell populations that are the source of these changes. Bio-markers can then be interpreted via a lattice-based visualization tool that depicts how these bio-markers affect each cell population and how they differentiate between samples of different phenotypes.

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