Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers
Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers
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DOI：
10.1371/journal.pcbi.1007925
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发表时间：
2020-06-01
影响因子：
4.3
通讯作者：
Li, Mingyao
中科院分区：
文献类型：
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作者：
Hu, Yu;Wang, Kai;Li, Mingyao
单细胞RNA-seq （scRNA-seq）技术的出现使得在细胞水平上测量基因表达变化成为可能。这一突破使研究更广泛的问题成为可能，包括分析单个细胞之间的剪接异质性。然而，与大量RNA-seq相比，由于高技术变异性和低测序深度，scRNA-seq数据噪声更大。在这里，我们提出SCATS（单细胞转录剪接分析）用于scRNA-seq的差异剪接分析，该方法考虑到技术噪声，在低覆盖率下实现了高灵敏度。SCATS对scRNA-seq数据进行建模，无论是否带有唯一分子标识符（UMIs）。对于非umi数据，SCATS通过使用外部尖峰输入来考虑捕获效率和放大偏差，从而明确地模拟技术噪声；对于UMI数据，SCATS模型捕获效率并进一步解释转录突发性。SCATS的一个关键方面在于它能够对来自相同同种异构体的“外显子”进行分组。外显子分组在scRNA-seq数据的剪接分析中是必不可少的，因为它自然地聚集了不同外显子的剪接读数，即使在测序深度较低的情况下也可以检测剪接事件。为了评估SCATS的性能，我们分析了模拟和真实的scRNA-seq数据集，并与现有的方法（包括Census和DEXSeq）进行了比较。我们发现SCATS具有良好的I型错误率控制，并且比现有方法更强大，特别是在拼接差异很小的情况下。相比之下，普查局遭受严重的I型错误通货膨胀，而DEXSeq则更为保守。当应用于小鼠脑scRNA-seq数据集时，与Census和DEXSeq相比，SCATS发现了更多不同细胞类型的细微差异剪接事件。随着scRNA-seq的日益普及，我们相信SCATS将非常适合各种剪接研究。
The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group "exons" that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies.