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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-7963045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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