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Iterative point set registration for aligning scRNA-seq data

Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date n...

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Detalles Bibliográficos
Autores principales: Alavi, Amir, Bar-Joseph, Ziv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647120/
https://www.ncbi.nlm.nih.gov/pubmed/33108369
http://dx.doi.org/10.1371/journal.pcbi.1007939
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author Alavi, Amir
Bar-Joseph, Ziv
author_facet Alavi, Amir
Bar-Joseph, Ziv
author_sort Alavi, Amir
collection PubMed
description Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date no method was able to both perform the alignment using the original expression space and generalize to new data. To enable such analysis we developed Single Cell Iterative Point set Registration (SCIPR) which extends methods that were successfully applied to align image data to scRNA-Seq. We discuss the required changes needed, the resulting optimization function, and algorithms for learning a transformation function for aligning data. We tested SCIPR on several scRNA-Seq datasets. As we show it successfully aligns data from several different cell types, improving upon prior methods proposed for this task. In addition, we show the parameters learned by SCIPR can be used to align data not used in the training and to identify key cell type-specific genes.
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spelling pubmed-76471202020-11-16 Iterative point set registration for aligning scRNA-seq data Alavi, Amir Bar-Joseph, Ziv PLoS Comput Biol Research Article Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date no method was able to both perform the alignment using the original expression space and generalize to new data. To enable such analysis we developed Single Cell Iterative Point set Registration (SCIPR) which extends methods that were successfully applied to align image data to scRNA-Seq. We discuss the required changes needed, the resulting optimization function, and algorithms for learning a transformation function for aligning data. We tested SCIPR on several scRNA-Seq datasets. As we show it successfully aligns data from several different cell types, improving upon prior methods proposed for this task. In addition, we show the parameters learned by SCIPR can be used to align data not used in the training and to identify key cell type-specific genes. Public Library of Science 2020-10-27 /pmc/articles/PMC7647120/ /pubmed/33108369 http://dx.doi.org/10.1371/journal.pcbi.1007939 Text en © 2020 Alavi, Bar-Joseph 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
Alavi, Amir
Bar-Joseph, Ziv
Iterative point set registration for aligning scRNA-seq data
title Iterative point set registration for aligning scRNA-seq data
title_full Iterative point set registration for aligning scRNA-seq data
title_fullStr Iterative point set registration for aligning scRNA-seq data
title_full_unstemmed Iterative point set registration for aligning scRNA-seq data
title_short Iterative point set registration for aligning scRNA-seq data
title_sort iterative point set registration for aligning scrna-seq data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647120/
https://www.ncbi.nlm.nih.gov/pubmed/33108369
http://dx.doi.org/10.1371/journal.pcbi.1007939
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