European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
1] European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. [2] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Nat Rev Genet. 2015 Mar;16(3):133-45. doi: 10.1038/nrg3833. Epub 2015 Jan 28.
The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.
高通量 RNA 测序 (RNA-seq) 在单细胞水平上的发展已经在生物学领域带来了深远的新发现，从新型细胞类型的鉴定到随机基因表达的全局模式研究。除了促进大规模生成单细胞转录组数据的技术突破之外，考虑仍然需要克服的具体计算和分析挑战也很重要。尽管一些用于分析批量细胞群体 RNA-seq 数据的工具可以很容易地应用于单细胞 RNA-seq 数据，但需要许多新的计算策略来充分利用这种数据类型，并能够全面而详细地研究单细胞水平的基因表达。
