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入见解，但比较这些分布仍面临挑战。我们提出一种核检验框架，用于进行非线性的细胞层级分布比较，可分析基因表达和表观基因组修饰。该方法支持特征级和全转录组/表观基因组水平比较，揭示细胞群异质性。通过基于嵌入变异性的分类器，我们能够识别细胞状态转变，克服传统单细胞分析的局限性。将本方法应用于单细胞ChIP-Seq数据，可识别出具有类似持久性细胞表观基因组特征的未治疗乳腺癌细胞。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。