PyLiger: scalable single-cell multi-omic data integration in Python
PyLiger: scalable single-cell multi-omic data integration in Python
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DOI：
10.1093/bioinformatics/btac190
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发表时间：
2022-03-31
期刊：
影响因子：
5.8
通讯作者：
Welch, Joshua D.
中科院分区：
文献类型：
--
作者：
Lu, Lu;Welch, Joshua D.
动机：LIGER(基因组实验关系的链接推理)是一个广泛使用的R包，用于单细胞多组数据集成。然而，许多用户倾向于在Python中分析他们的单细胞数据集，它提供了吸引人的语法和高度优化的科学计算库，从而提高了效率。结果：我们开发了一个用于集成单细胞多基因组数据集的Python包。与以前的R实现相比，PyLiger提供了更快的性能(加速2-5倍)、与AnnData格式的互操作性、灵活的磁盘或内存分析能力以及用于基因本体丰富分析的新功能。磁盘上的功能允许使用固定内存分析任意大的单单元数据集。
Motivation: LIGER (Linked Inference of Genomic Experimental Relationships) is a widely used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly optimized scientific computing libraries for increased efficiency.Results: We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5x speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.