Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

翻译：单细胞技术为分子特征分布提供了深刻见解，但比较这些分布存在挑战。我们提出了一种基于核检验的框架，用于非线性细胞水平分布比较，分析基因表达和表观基因组修饰。该方法允许进行特征层面和全局转录组/表观基因组比较，揭示细胞群异质性。通过利用基于嵌入变异性的分类器，我们识别细胞状态的转变，克服了传统单细胞分析的局限性。应用于单细胞ChIP-Seq数据时，我们的方法能够识别出具有类似持久细胞表观基因组谱的未处理乳腺癌细胞。这证明了核检验在揭示其他方法可能遗漏的细微群体变异方面的有效性。