Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Nat Methods. 2021 Jul;18(7):723-732. doi: 10.1038/s41592-021-01171-x. Epub 2021 Jun 21.
The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.
过去十年中，单细胞转录组测序方案取得了快速进展，同时用于分析此类数据的计算方法也取得了同样令人瞩目的进步。随着实验技术的容量和准确性的提高，新兴的算法发展揭示了潜在生物学的日益复杂的方面，从细胞类型组成到基因调控到发育动态。与此同时，快速发展迫使人们不断重新评估这些计算工具所处理的基础统计模型、实验目标和大量数据处理。在这里，我回顾了单细胞 RNA 测序 (scRNA-seq) 分析的关键计算步骤，检查了不同方法所做的假设，并强调了成功、仍然存在的不确定性和局限性，这些都是 scRNA-seq 成为生物学研究主流技术时需要牢记的重要因素。
