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Automated identification of maximal differential cell populations in flow cytometry data
Author(s) -
Yue Alice,
Chauve Cedric,
Libbrecht Maxwell W.,
Brinkman Ryan R.
Publication year - 2022
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.24503
Subject(s) - bioconductor , computer science , population , visualization , identification (biology) , r package , flow cytometry , data visualization , class (philosophy) , computational biology , data mining , artificial intelligence , biology , genetics , medicine , botany , environmental health , gene , computational science
We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice‐based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub ( github.com/aya49/flowGraph ) and on BioConductor.