Department of Biotechnology, Delhi Technological University, Delhi 110042, India.
Department of Biotechnology, Delhi Technological University, Delhi 110042, India.
Genomics. 2021 Mar;113(2):606-619. doi: 10.1016/j.ygeno.2021.01.007. Epub 2021 Jan 22.
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）是大数据时代的一项杰出成就，它以惊人的单细胞分辨率积累了大量的细胞转录组数据。它为以前通过批量细胞分析无法实现的细胞提供了有价值的见解，并在揭示细胞异质性、鉴定稀有细胞群、不同的细胞谱系轨迹以及参与复杂细胞过程的机制方面证明是至关重要的。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 工作流程的步骤，从实验方案到数据分析，讨论了使用的各种管道。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的前景。最后，我们列出了 scRNA-seq 数据的多种应用，以及它将如何彻底改变我们对细胞生物学和疾病的理解。
