Automated identification of maximal differential cell populations in flow cytometry data

Cytometry A. 2021 Sep 24. doi: 10.1002/cyto.a.24503. Online ahead of print.

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.

Keywords: automated analysis; bioinformatics; exploratory data analysis; flow cytometry; statistical analysis.