cytometree: A binary tree algorithm for automatic gating in cytometry analysis

Flow cytometry is a powerful technology that allows the high‐throughput quantification of dozens of surface and intracellular proteins at the single‐cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity...

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Vydané v:Cytometry. Part A Ročník 93; číslo 11; s. 1132 - 1140
Hlavní autori: Commenges, Daniel, Alkhassim, Chariff, Gottardo, Raphael, Hejblum, Boris, Thiébaut, Rodolphe
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 01.11.2018
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Predmet:
Algorithms
Annotations
Applications
automated gating
Automation
binary tree
Consortia
Data analysis
Data processing
Flow cytometry
Gating
Identification methods
Immunology
Life Sciences
Lymphocytes T
Machine Learning
mixture of distributions
Populations
Proteins
Quantitative Methods
Statistics
Subpopulations
Vaccinology
mixture of distributions
binary tree
flow cytometry
automated gating
Flow cytometry
Mixture of distributions
Binary tree
Automated gating
ISSN:1552-4922, 1552-4930, 1552-4930
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Popis
Shrnutí:Flow cytometry is a powerful technology that allows the high‐throughput quantification of dozens of surface and intracellular proteins at the single‐cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time‐consuming nature. We present a new unsupervised algorithm called “cytometree” to perform automated population identification (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distributions. Node splitting is done according to a model selection procedure based on a normalized difference of Akaike information criteria between two competing models. Post‐processing of the tree structure and derived populations allows us to complete the annotation of the populations. The algorithm is shown to perform better than the state‐of‐the‐art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods project. The algorithm is also applied to a T‐cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open‐source available algorithm while requiring the shortest computation time. © 2018 International Society for Advancement of Cytometry
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ISSN:1552-4922
1552-4930
1552-4930
DOI:10.1002/cyto.a.23601