Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.

单细胞转录组学 (SCT) 是大组学数据时代的一项壮举，它以惊人的单细胞分辨率积累了大量的细胞转录数据。它提供了对以前通过大量细胞分析无法实现的细胞的有价值的见解，并且在揭示细胞异质性、识别稀有细胞群、不同的细胞谱系轨迹以及复杂细胞过程中涉及的机制方面被证明是至关重要的。SCT 数据非常复杂，需要先进的统计和计算方法进行分析。本综述全面概述了典型 SCT 工作流程中的步骤，从实验方案到数据分析，审议了使用的各种管道。我们讨论了最近的趋势、挑战、用于数据分析的机器学习方法以及未来前景。