MOTIVATION Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. RESULTS We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. AVAILABILITY AND IMPLEMENTATION The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

---

合适的表达：具有捕获效率调节功能的单细胞RNA-seq数据的差异表达。

动机辍学是单细胞RNA-seq（scRNA-seq）数据中的常见现象，如果不加以解决，则会影响统计分析的有效性。尽管如此，目前很少有用于scRNA-seq数据差异表达（DE）分析的方法明确地对引起缺失事件的过程进行建模。我们开发了DECENT，一种用于对scRNA-seq数据进行DE分析的方法，该方法可显式准确地对scRNA-seq实验中的分子捕获过程进行建模。结果我们表明，相对于未明确建模辍学的现有DE方法，DECENT展示了改进的DE性能。在使用不同技术平台生成的几个公共scRNA-seq数据集中，始终可以观察到这种改进。当捕获过程过度分散时，改进的收益特别大。DECENT可以很好地保持I型错误，同时获得更高的灵敏度。在不使用尖峰插入的情况下，其性能几乎与使用尖峰插入来校准捕获模型时的性能一样好。可用性和实现该方法作为可从https://github.com/cz-ye/DECENT获得的可公开获得的R包来实现。补充信息补充数据可从Bioinformatics在线获得。