Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Yuanchao, Kim, Man S., Reichenberger, Erin R., Stear, Ben, Taylor, Deanne M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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
_version_ 1783532609598390272
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
work_keys_str_mv AT zhangyuanchao scedarascalablepythonpackageforsinglecellrnaseqexploratorydataanalysis
AT kimmans scedarascalablepythonpackageforsinglecellrnaseqexploratorydataanalysis
AT reichenbergererinr scedarascalablepythonpackageforsinglecellrnaseqexploratorydataanalysis
AT stearben scedarascalablepythonpackageforsinglecellrnaseqexploratorydataanalysis
AT taylordeannem scedarascalablepythonpackageforsinglecellrnaseqexploratorydataanalysis