Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data.

单细胞转录组学提供了一种揭示单个细胞异质性的有力方法。迄今为止，已经提出了许多信息理论方法来评估多样性和相似性，并表征转录组数据中潜在的异质性。多样性意味着基因表达的变化，可以促进特征基因的鉴定；同时，相似性揭示了细胞类型聚类的共表达模式。在这篇综述中，我们总结了用于评估单细胞转录组数据多样性和相似性的信息论的16种措施，为在一般情况下需要选择适当的测量方法时提供了参考并阐明了选定的理论特性。我们进一步提供了一个R包组装讨论的方法，以改善研究人员自己的单细胞转录组研究。