Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.

中文翻译：

---

方差调整马哈拉诺比斯 (VAM)：一种快速、准确的细胞特异性基因集评分方法。

单细胞 RNA 测序 (scRNA-seq) 数据的统计分析受到高水平的技术噪音和夸大的零计数的阻碍。解决这些挑战的一种有前途的方法是基因组测试或通路分析，它可以通过将表达数据聚合到通路水平来减轻稀疏性和噪音，并提高解释和功效。不幸的是，针对批量转录组学优化的方法在 scRNA-seq 数据上表现不佳，并且单细胞特异性技术的进展受到限制。重要的是，没有现有的方法支持细胞水平的基因集推断。为了应对这一挑战，我们开发了一种新的基因集测试方法，即方差调整 Mahalanobis (VAM)，它与 ​​Seurat 框架集成，可以适应 scRNA-seq 数据的技术噪声、稀疏性和大样本量特征。VAM 方法计算细胞特异性通路评分，将细胞基因矩阵转换为细胞通路矩阵，可用于数据可视化和统计富集分析。由于这些分数在不相关技术噪声的影响下的分布具有精确的伽马近似值，因此支持群体和细胞水平的推断。正如使用模拟和真实 scRNA-seq 数据所证明的那样，相对于现有的单样本基因集测试方法，VAM 方法以较低的计算成本提供了卓越的分类精度。