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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.
Afiliación
  • Zhang Y; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Kim MS; Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America.
  • Reichenberger ER; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Stear B; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Taylor DM; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol ; 16(4): e1007794, 2020 04.
Article en En | MEDLINE | ID: mdl-32339163
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.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional / Análisis de la Célula Individual / RNA-Seq Límite: Animals / Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional / Análisis de la Célula Individual / RNA-Seq Límite: Animals / Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos