Department of Biological and Biomedical Sciences, Rowan University, Glassboro, New Jersey 08028, USA.
Moorestown High School, Moorestown, New Jersey 08057, USA.
Genome Res. 2024 Oct 29;34(10):1636-1650. doi: 10.1101/gr.278843.123.
High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the -value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.
高通量测序 (HTS) 技术在研究整体和单细胞水平的生物学问题方面发挥了重要作用。比较两个 HTS 数据集通常依赖于测试两个负二项分布（DOTNB）差异的统计显著性。尽管负二项分布已经得到了很好的研究，但 DOTNB 的理论结果在很大程度上仍未得到探索。在这里，我们推导出 DOTNB 的基本分析结果，并研究其渐近性质。作为 DOTNB 的一项先进应用，我们引入了 DEGage，这是一种用于检测 scRNA-seq 数据中差异表达基因 (DEGs) 的计算方法。DEGage 计算基因表达水平的样本间差异的平均值作为检验统计量，并通过计算 DOTNB 的 - 值来确定显著的差异表达。使用模拟和真实的 scRNA-seq 数据集进行广泛验证表明，DEGage 优于五种流行的 DEG 分析工具：DEGseq2、DEsingle、edgeR、Monocle3 和 scDD。DEGage 对高辍学水平具有鲁棒性，并且在应用于平衡和不平衡数据集时表现出优异的灵敏度，即使样本量较小也是如此。我们利用 DEGage 分析前列腺癌 scRNA-seq 数据集，并鉴定出 17 种细胞类型的标记基因。此外，我们将 DEGage 应用于具有和不具有恐惧记忆的小鼠神经元 scRNA-seq 数据集，并揭示了之前分析中忽略的八个潜在的与记忆相关的基因。DOTNB 的理论结果和支持软件可广泛应用于 HTS 中离散计数数据的比较分析和广泛的研究问题。
