Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Division of Medical Oncology, University of Colorado Cancer Center, School of Medicine, University of Colorado, Aurora, CO, USA.
Nat Comput Sci. 2024 Mar;4(3):237-250. doi: 10.1038/s43588-024-00597-5. Epub 2024 Mar 4.
Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.
单细胞技术使我们能够对表型定义分子机制进行高分辨率研究。然而，数据稀疏和细胞异质性使得对单细胞样本中的生物变异性进行建模变得困难。在这里，我们提出了 SCORPION，这是一种工具，它使用消息传递算法从单细胞/核 RNA 测序数据中重建可比的基因调控网络，通过利用相同的基线先验，该网络适用于群体水平的比较。使用合成数据，我们发现 SCORPION 优于 12 种现有的基因调控网络重建技术。通过监督实验，我们表明 SCORPION 可以准确识别野生型和转录因子扰动细胞之间调控网络的差异。我们使用包含来自结直肠癌和相邻健康组织的 200,436 个细胞的单细胞 RNA 测序图谱展示了 SCORPION 的可扩展性，以进行群体水平分析。SCORPION 检测到的肿瘤区域之间的差异在多个队列中以及我们对疾病进展的理解中都是一致的，并阐明了可能影响患者生存的表型调节剂。
