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

Details

Serval ID
serval:BIB_C4E1F1B81793
Type
Article: article from journal or magazin.
Collection
Publications
Title
cytometree: A binary tree algorithm for automatic gating in cytometry analysis.
Journal
Cytometry. Part A
Author(s)
Commenges D., Alkhassim C., Gottardo R., Hejblum B., Thiébaut R.
ISSN
1552-4930 (Electronic)
ISSN-L
1552-4922
Publication state
Published
Issued date
11/2018
Peer-reviewed
Oui
Volume
93
Number
11
Pages
1132-1140
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
Keywords
Algorithms, Biomarkers/metabolism, Computational Biology/methods, Data Interpretation, Statistical, Flow Cytometry/methods, Humans, Immunophenotyping/methods, Normal Distribution, automated gating, binary tree, flow cytometry, mixture of distributions
Pubmed
Web of science
Open Access
Yes
Create date
28/02/2022 11:45
Last modification date
23/03/2024 7:24
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