Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Methods Mol Biol. 2021;2243:311-325. doi: 10.1007/978-1-0716-1103-6_16.
Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.
在过去的十年中，单细胞 RNA 测序（scRNAseq）成为分析细胞异质性和细胞特异性表达差异的一种越来越可行的解决方案。虽然不如批量测序成熟或完全实现，但新开发的计算方法为 scRNAseq 数据分析的挑战提供了一种解决方案，以前所未有的细节水平提供了以前无法获得的生物学见解。在这里，我们讨论了单细胞数据分析的固有挑战以及用于克服这些挑战的计算方法。我们涵盖了 scRNAseq 在细胞动力学研究中的当前和未来应用，以及作为生物学研究综合组成部分的应用。
