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

Yuanchao Zhang, Man S Kim, Erin R Reichenberger, Ben Stear and Deanne M Taylor

PLOS Computational Biology, 2020, vol. 16, issue 4, 1-24

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007794

DOI: 10.1371/journal.pcbi.1007794

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