An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

单细胞技术的爆炸式增长揭示了以前未被充分认识的细胞类型异质性以及与性别、疾病、发育和其他过程之间新的细胞状态关联。从转录组分析开始，单细胞技术已扩展到多组学方法，现在可以同时测量数据模式和空间细胞背景。现在可获取数百万个细胞、全基因组测量和多种模式的数据。尽管对此类多模态数据集的分析有可能为无法用单一分析模式推断的生物过程提供新的见解，但将非常大、复杂的多模态数据集成到生物模型和机制中是一个相当大的挑战。需要了解数据集成和可视化方法的原理，才能确定哪些方法最适合特定的单细胞数据集。每一类方法在实现各种生物学目标（包括细胞类型分类、调控网络建模和生物过程推断）的能力方面都有优点和缺点。在选择数据集成策略时，必须考虑多组学数据是否匹配（即在同一细胞上测量）或不匹配（即在不同细胞上测量），更重要的是整体建模和分析综合分析的可视化目标。