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Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which ofte...

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Autores principales: Fujii, Takeru, Maehara, Kazumitsu, Fujita, Masatoshi, Ohkawa, Yasuyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641884/
https://www.ncbi.nlm.nih.gov/pubmed/34797848
http://dx.doi.org/10.1371/journal.pcbi.1009579
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author Fujii, Takeru
Maehara, Kazumitsu
Fujita, Masatoshi
Ohkawa, Yasuyuki
author_facet Fujii, Takeru
Maehara, Kazumitsu
Fujita, Masatoshi
Ohkawa, Yasuyuki
author_sort Fujii, Takeru
collection PubMed
description Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.
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spelling pubmed-86418842021-12-04 Discriminative feature of cells characterizes cell populations of interest by a small subset of genes Fujii, Takeru Maehara, Kazumitsu Fujita, Masatoshi Ohkawa, Yasuyuki PLoS Comput Biol Research Article Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods. Public Library of Science 2021-11-19 /pmc/articles/PMC8641884/ /pubmed/34797848 http://dx.doi.org/10.1371/journal.pcbi.1009579 Text en © 2021 Fujii et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fujii, Takeru
Maehara, Kazumitsu
Fujita, Masatoshi
Ohkawa, Yasuyuki
Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title_full Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title_fullStr Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title_full_unstemmed Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title_short Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
title_sort discriminative feature of cells characterizes cell populations of interest by a small subset of genes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641884/
https://www.ncbi.nlm.nih.gov/pubmed/34797848
http://dx.doi.org/10.1371/journal.pcbi.1009579
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