CellDEEP 用细胞聚合策略改进 scRNA-seq 差异表达分析
Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq
CellDEEP 是一个基于细胞聚合（metacell）的 scRNA-seq 差异表达分析工具，提供可灵活选择聚合策略与参数化的框架。作者指出，单细胞特异性统计模型常报告大量候选基因，但假阳性率可能偏高；pseudobulk 方法虽能改善错误发现控制，却牺牲了灵敏度。在模拟数据和包括 COVID-19、类风湿关节炎在内的真实数据集上做基准评测，CellDEEP 往往优于其他方法，相比单细胞方法持续降低假阳性，并比 pseudobulk 方法找回更多真阳性。作者称其思路是把重点从挑选单一最优方法转向降低细胞水平噪声同时保留生物学信号，并配套透明的验证框架。
来源：NAR Genomics and Bioinformatics · doi.org
Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing data remains challenging. Single-cell-specific statistical models often report large numbers of candidate genes but can exhibit inflated false positive rates, whereas pseudobulk approaches improve false discovery control at the cost of reduced sensitivity. To overcome the noise and bias that other tools have, and allow the user to have more control of the DEG process, we present CellDEEP, which uses a cell aggregation (metacell) approach. This tool provides a framework for flexible selection of pooling strategies and parameterization for differential expression analysis. Benchmarking on simulated and real datasets, including COVID-19 and rheumatoid arthritis, shows that CellDEEP often outperforms other methods, consistently reduces false positives compared to single-cell methods and recovers more true positives than pseudobulk methods. Our work shifts the focus from selecting a single ‘best’ method to an approach that reduces cell-level noise while preserving biological signal, together with transparent validation framework, advancing more reliable differential-expression analysis in single-cell transcriptomics.