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.
Highlights
Cost-effective large-scale transcriptomic profiling of individual cells is enabled by the development of microfluidic, nanodroplet, and massively parallel sequencing technologies
We illustrate the basic workflow of using scedar for scRNA-seq exploratory data analysis with the dataset published by Zeisel et al [53] (Fig 1)
The biological results of scRNA-seq experiments are obtained from extensive data analyses, which could take more time than doing the experiments
Summary
Cost-effective large-scale transcriptomic profiling of individual cells is enabled by the development of microfluidic, nanodroplet, and massively parallel sequencing technologies. Using these technologies, single-cell RNA-seq (scRNA-seq) experiments usually generate transcriptomic profiles of thousands to millions of individual cells [1]. Analyses of scRNA-seq datasets require efficient computational programs and sophisticated statistical methods. The programs should be able to manage memory efficiently, exploit multiple cores of the processing units, and handle errors and exceptions gracefully. The statistical methods must be able to function against high dimensionality, low signal-to-noise ratio, and different characteristics of data generated from different technologies and protocols
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