While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation.

中文翻译：

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癌症研究中的多组学方法及其在肿瘤亚型、预后和诊断中的应用


虽然经济高效的高通量技术提供了越来越多的数据，但单层数据的分析很少提供因果关系。跨不同细胞功能水平（包括基因组、表观基因组、转录组、蛋白质组、代谢组和微生物组）的多组学数据集成策略为了解癌症等复杂疾病的基础生物学提供了无与伦比的机会。我们回顾了一些最常用的数据集成方法，并概述了多组学显着有助于我们理解恶性转化过程和结果的研究领域。