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JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters

With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from...

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Detalles Bibliográficos
Autores principales: Lim, Hong Seo, Qiu, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963045/
https://www.ncbi.nlm.nih.gov/pubmed/33724985
http://dx.doi.org/10.1371/journal.pcbi.1008804
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author Lim, Hong Seo
Qiu, Peng
author_facet Lim, Hong Seo
Qiu, Peng
author_sort Lim, Hong Seo
collection PubMed
description With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps—low-dimensional discretized representation of datasets–that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering.
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spelling pubmed-79630452021-03-25 JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters Lim, Hong Seo Qiu, Peng PLoS Comput Biol Research Article With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps—low-dimensional discretized representation of datasets–that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering. Public Library of Science 2021-03-16 /pmc/articles/PMC7963045/ /pubmed/33724985 http://dx.doi.org/10.1371/journal.pcbi.1008804 Text en © 2021 Lim, Qiu http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Lim, Hong Seo
Qiu, Peng
JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title_full JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title_fullStr JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title_full_unstemmed JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title_short JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
title_sort jsom: jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963045/
https://www.ncbi.nlm.nih.gov/pubmed/33724985
http://dx.doi.org/10.1371/journal.pcbi.1008804
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