Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.

解锁单细胞 RNA 测序数据 (scRNAseq) 的全维度是更丰富、更全面地了解细胞生物学的下一个前沿领域。我们引入了q - diffusion，这是一个用于捕获整个基因库的共表达结构的框架，改进了最先进的分析工具。该方法通过三个案例研究得到证明。首先，在分析 CALGB/SWOG 80405 随机 III 期临床试验时， q扩散有助于获得对患者结果的差异效应的统计显着性，为转移性结直肠癌的治疗提供精确指导。其次，q-扩散使用体外 PBMC 数据集与现有的 scRNAseq 分类方法进行基准测试，其中所提出的方法更准确地区分 IFN- γ刺激。同一个案例研究证明了最近的 Tabula Sapiens 人类图谱在无监督细胞聚类方面的改进。最后，由q扩散驱动的空间 scRNAseq 的局部分布分割方法产生了可解释的人类皮质组织结构。