Automated identification of maximal differential cell populations in flow cytometry data. Issue 2 (22nd October 2021)

• Record Type: Journal Article
• Authors: Yue, Alice; Chauve, Cedric; Libbrecht, Maxwell W.; Brinkman, Ryan R.
• Is Part Of: Cytometry. Volume 101:Issue 2(2022)
• Page Start: 177
• Page End: 184
• Publication Date: 2021-10-22
• Subjects: automated analysis -- bioinformatics -- exploratory data analysis -- flow cytometry -- statistical analysis; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.24503
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: 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.