标题： 多组学数据整合方法的技术综述：从经典统计方法到深度生成方法 显示英文标题

标题： A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches

摘要： 高通量测序和其他检测技术的快速发展导致了大型和复杂的多组学数据集的生成，为推进精准医学策略提供了前所未有的机会。 然而，由于数据类型之间的高维度、异质性、实验缺口以及缺失值的频率，多组学数据整合面临着重大挑战。 计算方法已被开发用于解决这些问题，采用统计和机器学习方法来揭示复杂的生物模式，并提供对我们理解疾病机制的更深入见解。 在此，我们全面回顾了最先进的多组学数据整合方法，重点介绍了深度生成模型，特别是广泛用于数据填补和增强、联合嵌入创建和批次效应校正的变分自编码器（VAEs）。 我们探讨了损失函数和正则化技术的技术方面，包括对抗训练、解缠和对比学习。 此外，我们讨论了基础模型的最新进展以及新兴数据模态的整合，同时描述了当前的局限性，并概述了增强生物医学研究中多模态方法的未来方向。 显示英文摘要

摘要： The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine strategies. However, multi-omics data integration presents significant challenges due to the high dimensionality, heterogeneity, experimental gaps, and frequency of missing values across data types. Computational methods have been developed to address these issues, employing statistical and machine learning approaches to uncover complex biological patterns and provide deeper insights into our understanding of disease mechanisms. Here, we comprehensively review state-of-the-art multi-omics data integration methods with a focus on deep generative models, particularly variational autoencoders (VAEs) that have been widely used for data imputation and augmentation, joint embedding creation, and batch effect correction. We explore the technical aspects of loss functions and regularisation techniques including adversarial training, disentanglement and contrastive learning. Moreover, we discuss recent advancements in foundation models and the integration of emerging data modalities, while describing the current limitations and outlining future directions for enhancing multi-modal methodologies in biomedical research.