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Abstract：
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.
摘要：
单细胞分辨系统生物学方法，包括基于物质和成像的测量模式，生成大量表征细胞群体异质性的高维数据。表示学习方法通常用于分析这些复杂的，通过将高维数据投影到低维嵌入中。这有助于结构的解释和询问，动力学,和细胞异质性的调节。反映了它们在分析不同单细胞数据类型中的核心作用，存在无数的表征学习方法，新方法不断涌现。这里,我们对比了跨越统计的表示学习方法的一般特征，流形学习，和神经网络方法。我们考虑使用单细胞数据表示学习中涉及的关键步骤，包括数据预处理，超参数优化，下游分析，和生物验证。还强调了将这些步骤联系起来的相互依存关系和突发事件。此概述旨在指导研究人员进行选择，应用程序,以及当前和未来单细胞研究应用的表征学习策略的优化。
