A fast, scalable and versatile tool for analysis of single-cell omics data.
A fast, scalable and versatile tool for analysis of single-cell omics data.
复制标题
一种快速、可扩展且多功能的工具，用于分析单细胞组学数据。
DOI：
10.1038/s41592-023-02139-9
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发表时间：
2024
期刊：
影响因子：
48
通讯作者：
Ren,Bing
中科院分区：
文献类型：
--
作者：
Zhang,Kai;Zemke,NathanR;Armand,EthanJ;Ren,Bing
单细胞组学技术已经彻底改变了复杂组织中基因调控的研究。在分析这些数据集时，一个主要的计算挑战是将大规模和高维数据投影到低维空间中，同时保持细胞之间的相对关系。这种低维嵌入对于分解细胞异质性和重建细胞类型特异性基因调控程序是必要的。然而，传统的降维技术在计算效率和全面解决不同分子模式的细胞多样性方面面临挑战。在这里，我们引入了一种非线性降维算法，体现在Python包SnapATAC2中，它不仅实现了更精确的单细胞组学数据异构捕获，而且还确保了高效的运行时和内存使用，并随着细胞数量线性扩展。我们的算法在不同的单细胞组学数据集上展示了卓越的性能，可扩展性和多功能性，包括使用测序的转座酶可访问染色质的单细胞分析，单细胞RNA测序，单细胞Hi-C和单细胞多组学数据集，强调了其在推进单细胞分析方面的实用性。
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.