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Machine learning for cell type classification from single nucleus RNA sequencing data
With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506651/ https://www.ncbi.nlm.nih.gov/pubmed/36149937 http://dx.doi.org/10.1371/journal.pone.0275070 |
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author | Le, Huy Peng, Beverly Uy, Janelle Carrillo, Daniel Zhang, Yun Aevermann, Brian D. Scheuermann, Richard H. |
author_facet | Le, Huy Peng, Beverly Uy, Janelle Carrillo, Daniel Zhang, Yun Aevermann, Brian D. Scheuermann, Richard H. |
author_sort | Le, Huy |
collection | PubMed |
description | With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods—logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)–as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines. |
format | Online Article Text |
id | pubmed-9506651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95066512022-09-24 Machine learning for cell type classification from single nucleus RNA sequencing data Le, Huy Peng, Beverly Uy, Janelle Carrillo, Daniel Zhang, Yun Aevermann, Brian D. Scheuermann, Richard H. PLoS One Research Article With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods—logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)–as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines. Public Library of Science 2022-09-23 /pmc/articles/PMC9506651/ /pubmed/36149937 http://dx.doi.org/10.1371/journal.pone.0275070 Text en © 2022 Le 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 Le, Huy Peng, Beverly Uy, Janelle Carrillo, Daniel Zhang, Yun Aevermann, Brian D. Scheuermann, Richard H. Machine learning for cell type classification from single nucleus RNA sequencing data |
title | Machine learning for cell type classification from single nucleus RNA sequencing data |
title_full | Machine learning for cell type classification from single nucleus RNA sequencing data |
title_fullStr | Machine learning for cell type classification from single nucleus RNA sequencing data |
title_full_unstemmed | Machine learning for cell type classification from single nucleus RNA sequencing data |
title_short | Machine learning for cell type classification from single nucleus RNA sequencing data |
title_sort | machine learning for cell type classification from single nucleus rna sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506651/ https://www.ncbi.nlm.nih.gov/pubmed/36149937 http://dx.doi.org/10.1371/journal.pone.0275070 |
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