Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, TX, USA.
Eli Lilly and Company, Lilly Corporate Center, 893 Delaware St, Indianapolis, IN, 46225, USA.
Hum Genomics. 2024 Jun 20;18(1):69. doi: 10.1186/s40246-024-00638-0.
Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive.
Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power and we showed in simulated scenario, the proposed BFH would be an optimal method when compared with other popular single cell differential expression methods if both FDR and power were considered. As an example, the method was applied to an idiopathic pulmonary fibrosis (IPF) case study.
In our IPF example, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.
单细胞 RNA 测序技术 (scRNA-seq) 已被证明可用于理解特定于细胞的疾病机制。然而，鉴定感兴趣的基因仍然是一个关键挑战。通常使用汇集相同生物学重复中的 scRNA-seq 计数的伪总体方法来鉴定差异表达基因。然而，由于 scRNA-seq 数据集的样本量有限，这种方法可能缺乏功效，这可能非常昂贵。
受此启发，我们提出使用贝叶斯-频率混合 (BFH) 框架来提高功效，并在模拟场景中表明，如果同时考虑 FDR 和功效，与其他流行的单细胞差异表达方法相比，所提出的 BFH 将是一种最优方法。作为一个例子，该方法应用于特发性肺纤维化 (IPF) 案例研究。
在我们的 IPF 示例中，我们证明了通过适当的信息先验，BFH 方法可以识别更多的感兴趣基因。此外，这些基因基于 IPF 的现有知识是合理的。因此，BFH 为未来的 scRNA-seq 分析提供了一个独特而灵活的框架。
