Department of Electrical Engineering & Computer Science, UC Berkeley, Berkeley, CA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Nat Methods. 2019 Feb;16(2):163-166. doi: 10.1038/s41592-018-0303-9. Epub 2019 Jan 21.
Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3' single-cell RNA-seq that can identify previously undetectable marker genes.
单细胞 RNA 测序使得对不同条件下的细胞类型的转录组进行特征分析，并通过差异分析识别其转录特征成为可能。我们的方法通过检测大量细胞中转录动态和整体基因丰度的变化来确定差异表达。当应用于通过伪比对获得的转录兼容性计数时，我们的方法提供了一种无定量分析的 3' 单细胞 RNA 测序分析，可以识别以前无法检测到的标记基因。
