Harmony-based data integration for distributed single-cell multi-omics data.

IF 3.6 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-09-30 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013526

Ruizhi Yuan, Ziqi Rong, Haoran Hu, Tianhao Liu, Shiyue Tao, Wei Chen, Lu Tang

引用次数: 0

Abstract

Large-scale single-cell projects generate rapidly growing datasets, but downstream analysis is often confounded by data sources, requiring data integration methods to do correction. Existing data integration methods typically require data centralization, raising privacy and security concerns. Here, we introduce Federated Harmony, a novel method combining properties of federated learning with Harmony algorithm to integrate decentralized omics data. This approach preserves privacy by avoiding raw data sharing while maintaining integration performance comparable to Harmony. Experiments on various types of single-cell data showcase superior results, highlighting a novel data integration approach for distributed multi-omics data without compromising data privacy or analytical performance.

Abstract Image

查看原文本刊更多论文

基于和谐的分布式单细胞多组学数据集成。

大规模的单细胞项目产生快速增长的数据集，但下游分析经常被数据源混淆，需要数据集成方法进行校正。现有的数据集成方法通常需要数据集中，这引起了隐私和安全方面的担忧。本文介绍了一种结合联邦学习特性和Harmony算法的分布式组学数据集成新方法——Federated Harmony。这种方法通过避免原始数据共享来保护隐私，同时保持与Harmony相当的集成性能。在不同类型的单细胞数据上的实验显示了优异的结果，突出了分布式多组学数据的新型数据集成方法，而不影响数据隐私或分析性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.