School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia.
Cell Rep Methods. 2023 Aug 2;3(8):100547. doi: 10.1016/j.crmeth.2023.100547. eCollection 2023 Aug 28.
Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.
单细胞解析系统生物学方法，包括基于组学和成像的测量模式，生成了大量描述细胞群体异质性的高维数据。表示学习方法通常用于通过将它们投影到低维嵌入中来分析这些复杂的高维数据。这有助于解释和探究细胞异质性的结构、动态和调控。反映了它们在分析各种单细胞数据类型中的核心作用，存在着大量的表示学习方法，新的方法不断涌现。在这里，我们对比了跨越统计、流形学习和神经网络方法的表示学习方法的一般特征。我们考虑了单细胞数据中表示学习涉及的关键步骤，包括数据预处理、超参数优化、下游分析和生物学验证。还强调了这些步骤之间的相互依存关系和偶然情况。这篇综述旨在为研究人员指导选择、应用和优化表示学习策略，以用于当前和未来的单细胞研究应用。
