scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data
scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data
复制标题
scSampler：大规模单细胞转录组数据的快速多样性保留子采样
DOI：
10.1093/bioinformatics/btac271
复制
发表时间：
2022
期刊：
影响因子：
5.8
通讯作者：
Vitek, ed., Olga
中科院分区：
文献类型：
--
作者：
Song, Dongyuan;Xi, Nan Miles;Li, Jingyi Jessica;Wang, Lin;Vitek, ed., Olga
摘要近年来，在单细胞转录数据中测量的细胞数量增长迅速。对于如此大规模的数据，二次抽样是探索性数据分析的强大且往往是必要的工具。然而，从保存稀有细胞类型的角度来看，最简单的随机二次抽样并不理想。因此，为了快速探索大规模数据集中的细胞类型，需要保持多样性的子采样。在此，我们提出了一种单细胞转录数据快速保持分集的子采样算法scSsamer，该算法的可用性和实现采用了Python语言，并以麻省理工学院源代码许可证的形式发布。它可以通过“PIP INSTALL SSAMPLER”安装，并与Scanpy管路一起使用。代码可在giHub上找到：https://github.com/SONGDONGYUAN1994/scsampler.R接口可在：https://github.com/SONGDONGYUAN1994/rscsampler.Supplementary INFORMATION上找到补充数据可在BioInformation Online上找到。
SummaryThe number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here, we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data.Availability and implementationscSampler is implemented in Python and is published under the MIT source license. It can be installed by “pip install scsampler” and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. An R interface is available at: https://github.com/SONGDONGYUAN1994/rscsampler.Supplementary informationSupplementary data are available atBioinformaticsonline.