Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a ‘lack of information’ problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

中文翻译：

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基因表达数据分析的垂直整合方法。


基因表达数据在许多生物医学研究中发挥着重要作用。当基因数量很大而样本量有限时，就会出现“信息缺乏”问题，导致结果质量低下。为了解决这个问题，水平和垂直数据整合已经被开发出来，其中垂直整合方法共同分析基因表达及其调节因子（例如突变、DNA甲基化和miRNA）的数据。在本文中，我们对基因表达数据的垂直数据整合方法进行了选择性回顾。审查的方法涵盖边际分析和联合分析以及监督和非监督分析。主要目标是提供垂直数据集成范例的草图，而不需要深入研究太多的技术细节。我们还简要讨论了潜在的陷阱、未来发展的方向和应用说明。