在许多利用单细胞 RNA 测序的研究中，推断生物途径和基因组的变化以及如何变化是一个关键问题。通常，这些问题是通过量化全局分析得出的基因列表中已知基因集的富集度来解决的。在这里，我们提供 SiPSiC，这是一种推断每个单细胞通路活性的新方法。这允许更灵敏的差异分析和利用通路分数来聚类细胞并计算 UMAP 或其他类似的预测。我们将我们的方法应用于 COVID-19、肺腺癌和神经胶质瘤数据集，并证明其实用性。 SiPSiC 分析结果与之前许多情况下的研究报告的结果一致，但 SiPSiC 还揭示了新途径的差异活性，使我们能够提出这些疾病病理生理学的新机制，并证明 SiPSiC 在检测生物功能和检测方面具有高精度和灵敏度。特征。此外，我们还展示了如何使用它根据生物途径的活性而不是单个基因更好地对细胞进行分类，以及它克服患者特异性伪影的能力。© 2024 Davis 等人；由冷泉港实验室出版社出版。

Inferring which and how biological pathways and gene sets change is a key question in many studies that utilize single-cell RNA sequencing. Typically, these questions are addressed by quantifying the enrichment of known gene sets in lists of genes derived from global analysis. Here we offer SiPSiC, a new method to infer pathway activity in every single cell. This allows more sensitive differential analysis and utilization of pathway scores to cluster cells and compute UMAP or other similar projections. We apply our method to COVID-19, lung adenocarcinoma and glioma data sets, and demonstrate its utility. SiPSiC analysis results are consistent with findings reported in previous studies in many cases, but SiPSiC also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiC's high accuracy and sensitivity in detecting biological function and traits. In addition, we demonstrate how it can be used to better classify cells based on activity of biological pathways instead of single genes and its ability to overcome patient-specific artifacts.© 2024 Davis et al.; Published by Cold Spring Harbor Laboratory Press.