全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2020 

Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis

DOI: 10.1371/journal.pcbi.1007794

Keywords: Transcriptome analysis,Data visualization,Statistical data,Algorithms,Machine learning algorithms,Computing methods,DNA transcription,Leaves

Full-Text   Cite this paper   Add to My Lib

Abstract:

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

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133