Literature DB >> 34559446

Automated identification of maximal differential cell populations in flow cytometry data.

Alice Yue1, Cedric Chauve2,3, Maxwell W Libbrecht1, Ryan R Brinkman4,5.   

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.
© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Entities:  

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

Mesh：

Substances：

Year:  2021        PMID： 34559446      PMCID： PMC8810629          DOI： 10.1002/cyto.a.24503

Source DB:  PubMed          Journal:  Cytometry A        ISSN： 1552-4922            Impact factor:   4.714


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  1 in total

1.  Automated identification of maximal differential cell populations in flow cytometry data.

Authors:  Alice Yue; Cedric Chauve; Maxwell W Libbrecht; Ryan R Brinkman
Journal:  Cytometry A       Date:  2021-10-22       Impact factor: 4.714
  1 in total

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