FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis

Linjie Wang

doi:10.1093/bib/bbaf157

2025-04-11

DOI：10.1093/bib/bbaf157

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摘要

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单细胞多组学技术通过同时表征单个细胞内的多个分子层，彻底改变了细胞状态和功能的研究。然而，现有用于整合这些数据的方法难以保留关键特征信息，也无法利用已知的调控知识，这对于理解细胞功能至关重要。这一局限性阻碍了它们提供对细胞全面且准确见解的能力。在此，我们提出FactVAE，一种创新的因子分解变分自编码器，旨在实现对单细胞多组学数据的稳健且准确的理解。FactVAE将因子分解原理整合到变分自编码器框架中，确保在保留特征信息的同时，利用神经网络对样本信息的非线性捕获。此外，在模型训练过程中纳入了已知的调控知识，并采用知识迁移策略用于细胞嵌入优化和数据增强。对不同协议来源的单细胞多组学数据集以及空间多组学数据集的比较分析表明，FactVAE不仅在聚类性能上优于基准方法，而且生成的增强数据揭示了最清晰的细胞类型特异性基序表达。此外，FactVAE捕获的特征嵌入能够推断潜在且可靠的基因调控关系。总体而言，FactVAE的卓越性能和强大的可扩展性使其成为单细胞多组学数据分析的有前景的新解决方案。

Keyword:

single-cell multi-omics data

factorization

variational autoencoder (VAE)

期刊

Briefings in Bioinformatics

IF:

7.7

论文数:

5.8K

被引数:

2.7W

机构

north eastern univ

学者数:

论文数: 3

被引数: 0

suny albany

学者数:

论文数: 1

被引数: 0

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