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Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis
In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217489/ https://www.ncbi.nlm.nih.gov/pubmed/32339163 http://dx.doi.org/10.1371/journal.pcbi.1007794 |
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author | Zhang, Yuanchao Kim, Man S. Reichenberger, Erin R. Stear, Ben Taylor, Deanne M. |
author_facet | Zhang, Yuanchao Kim, Man S. Reichenberger, Erin R. Stear, Ben Taylor, Deanne M. |
author_sort | Zhang, Yuanchao |
collection | PubMed |
description | In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar. |
format | Online Article Text |
id | pubmed-7217489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72174892020-05-29 Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis Zhang, Yuanchao Kim, Man S. Reichenberger, Erin R. Stear, Ben Taylor, Deanne M. PLoS Comput Biol Research Article In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar. Public Library of Science 2020-04-27 /pmc/articles/PMC7217489/ /pubmed/32339163 http://dx.doi.org/10.1371/journal.pcbi.1007794 Text en © 2020 Zhang et al 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 Zhang, Yuanchao Kim, Man S. Reichenberger, Erin R. Stear, Ben Taylor, Deanne M. Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title | Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title_full | Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title_fullStr | Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title_full_unstemmed | Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title_short | Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis |
title_sort | scedar: a scalable python package for single-cell rna-seq exploratory data analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217489/ https://www.ncbi.nlm.nih.gov/pubmed/32339163 http://dx.doi.org/10.1371/journal.pcbi.1007794 |
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