Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs—the reference panel and the chain-type designs—true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.

尽管应用广泛，但单细胞 RNA 测序 (scRNA-seq) 实验仍然受到批处理效应和丢失事件的困扰。尽管经常提倡完全随机的实验设计来控制批次效应，但由于时间和预算限制，它很少在实际应用中实施。在这里，我们从数学上证明，在两种更灵活和更现实的实验设计——参考面板和链式设计——下，真正的生物变异性也可以与批次效应分开。我们为 scRNA-seq 数据 (BUSseq) 开发了具有未知子类型的批量效应校正，这是一种可解释的贝叶斯层次模型，它密切遵循 scRNA-seq 实验的数据生成机制。BUSseq 可以同时校正批次效应、簇细胞类型、估算由丢失事件引起的缺失数据，并检测差异表达的基因，而无需初步的标准化步骤。我们用模拟和真实数据证明了 BUSseq 优于现有方法。