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Cytometry Part A
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Cytometry Part A
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https://doi.org/10.1101/837765...
Article . 2019 . Peer-reviewed
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Cytometry Part A
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Automated identification of maximal differential cell populations in flow cytometry data

Authors: Ryan R. Brinkman; Ryan R. Brinkman; Maxwell W. Libbrecht; Alice Yue; Cedric Chauve; Cedric Chauve;

Automated identification of maximal differential cell populations in flow cytometry data

Abstract

AbstractWe 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

Original Articles, Flow Cytometry, Biomarkers, Software

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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