调整单细胞多原子数据的跨模式整合和监管异质性表征,使用深度对比学习
Yue Cheng1,2, Yanchi Su1, Yi Fan1
1School of Artificial Intelligence, Jilin University, Changchun, Jilin, 130012, China.
Genome medicine
|January 26, 2026
概括
我们开发了scMDCF,这是一个用于单细胞多omics集成的深度学习模型. 它有效地协调了各种数据类型,揭示了对细胞功能和疾病机制的新见解,包括阿尔茨海默病.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 单单细胞多omics (scMulti-omics) 技术可以同时测量多种不同的细胞模式.
- 整合异构的scMulti-omics数据是具有挑战性的,因为规模,分辨率和生物变异性的差异.
- 传统方法往往无法调和这些差异,失去了关键的生物变异性和分子间相互作用.
研究的目的:
- 开发一种新的深度学习模型,以高效地描述和整合scMulti-omics数据.
- 解决数据异质性的挑战,并调和跨欧米克层的差异.
- 保持生物变异性,揭示微妙的分子间相互作用.
主要方法:
- 开发了一个使用对比学习的单细胞多omics深度学习模型 (scMDCF).
- 实施了一种跨模式的对比学习模块,以协调跨omics类型的数据表示.
- 使用交叉模式的特征融合模块提取常见的低维潜伏表示.
主要成果:
- 在各种数据类型中,scMDCF的性能优于现有的最先进的scMulti-omics模型.
- 从SNARE-seq数据中成功地提取了特定于细胞类型的峰值基因关联和cis-regulatory元素.
- 从CITE-seq数据和SARS-CoV-2疫苗接种后确定的疫苗诱导的B细胞亚群的阐明免疫调节.
- 在阿尔茨海默病数据中确定了少数微质细胞和内皮细胞种群,揭示了ELF1作为潜在的生物标志物.
结论:
- scMDCF是一个用于 scMulti-omics 集成的对比学习框架,在保持异质性的同时协调表示.
- 在多种scMulti-omics数据集中展示了改进的集群,批量效应缓解和机械洞察力.
- 为 scMulti-omics 数据分析提供公开可用的代码和可重复的工作流.
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