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大致近邻图为大规模生物数据的应用提供了快速高效的嵌入

Jianshu Zhao^1,2, Jean Pierre Both³, Konstantinos T Konstantinidis^1,2,4

¹Center for Bioinformatics and Computational Genomics, Georgia Institute of Technology, 225 North Avenue NW, Atlanta, GA, 30332, USA.

NAR genomics and bioinformatics

|December 20, 2024

概括

我们开发了一种嵌入式,用于大数据可视化的更快的维度减小 (DR) 算法. 它通过使用层次导航小世界图来改进类似UMAP的方法,以高效处理大型生物数据集.

科学领域:

计算生物学计算生物学
数据科学数据科学数据科学
生物信息学是一种生物信息学.

背景情况:

像t-SNE和UMAP这样的缩小维度 (DR) 算法对于大数据可视化至关重要,但在处理大型数据集时却存在困难.
现有的方法面临着计算瓶,特别是在构建K-近邻图 (K-NNG) 时.
需要适用于各种生物数据的可扩展的DR技术,包括基因组序列.

研究的目的:

为大规模的生物数据可视化开发一个改进和高效的维度减小算法.
通过优化K-NNG结构来提高DR方法的速度和可扩展性.
将DR功能扩展到DNA/RNA序列数据分析.

主要方法:

开发了一个新的DR算法,结合了t-SNE和UMAP的各个方面.
将K-NNG瓶取代为一个分层可导航的小世界 (HNSW) 图.
集成的HNSW与局部敏感哈希 (例如MinHash) 进行高效的DNA/RNA序列距离估计.

主要成果:

在Rust中实现的嵌入式库,与UMAP类算法相比,提供了具有竞争力的准确性.
在大规模微生物基因组学,单细胞RNA测序和元基因组组数据集上证明了可扩展性和有效性.
引入了计算本地内在维度和枢纽的功能,以评估嵌入质量.

结论:

嵌入式提供了一个快速而准确的解决方案,用于大生物数据的尺寸缩小.
该库方便分析任务,其中远程计算是密集的或数据集是巨大的.
安嵌是推动生物数据分析的宝贵工具,特别是在基因组学和单细胞研究中.

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