用于单细胞 RNA-seq 分析的贝叶斯-频数混合推断框架。

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY Human Genomics Pub Date : 2024-06-20 DOI:10.1186/s40246-024-00638-0
Gang Han, Dongyan Yan, Zhe Sun, Jiyuan Fang, Xinyue Chang, Lucas Wilson, Yushi Liu
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引用次数: 0

摘要

背景:单细胞 RNA 测序技术(scRNA-seq)已被证明有助于了解细胞特异性疾病机制。然而,识别感兴趣的基因仍是一项关键挑战。在相同生物重复序列中汇集 scRNA-seq 计数的伪大量方法常用于识别差异表达基因。然而,由于 scRNA-seq 数据集的样本量有限,这种方法可能缺乏威力,而且成本过高:受此启发,我们提出了使用贝叶斯-频率主义混合(BFH)框架来提高功率,并在模拟场景中表明,如果同时考虑FDR和功率,与其他流行的单细胞差异表达方法相比,所提出的BFH将是一种最佳方法。以特发性肺纤维化(IPF)病例研究为例,我们应用了该方法:在我们的 IPF 案例中,我们证明了在适当的信息先验条件下,BFH 方法能识别出更多的相关基因。此外,根据目前对 IPF 的了解,这些基因也是合理的。因此,BFH 为未来的 scRNA-seq 分析提供了一个独特而灵活的框架。
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Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.

Background: 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.

Results: 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.

Conclusion: 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.

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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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