Single-cell technologies have provided valuable insights into the distribution of molecular features, such as gene expression and epigenomic modifications. However, comparing these complex distributions in a controlled and powerful manner poses methodological challenges. Here we propose to benefit from the kernel-testing framework to compare the complex cell-wise distributions of molecular features in a non-linear manner based on their kernel embedding. Our framework not only allows for feature-wise analyses but also enables global comparisons of transcriptomes or epigenomes, considering their intricate dependencies. By using a classifier to discriminate cells based on the variability of their embedding, our method uncovers heterogeneities in cell populations that would otherwise go undetected. We show that kernel testing overcomes the limitations of differential analysis methods dedicated to single-cell. Kernel testing is applied to investigate the reversion process of differentiating cells, successfully identifying cells in transition between reversion and differentiation stages. Additionally, we analyze single-cell ChIP-Seq data and identify a subpopulation of untreated breast cancer cells that exhibit an epigenomic profile similar to persister cells.

翻译：单细胞技术为解析基因表达与表观基因组修饰等分子特征的分布提供了重要见解。然而，以受控且高效的方式比较这些复杂分布仍面临方法论挑战。本文提出利用核检验框架，基于分子特征的核嵌入以非线性方式比较复杂细胞层面分布。该框架不仅支持特征维度的分析，还能在考虑转录组或表观基因组复杂依赖关系的基础上实现全局比较。通过使用分类器根据细胞嵌入的变异性进行判别，本方法能够揭示细胞群体中原本难以检测的异质性。研究表明核检验突破了单细胞差异分析方法固有的局限性。我们将核检验应用于分化细胞的逆转过程研究，成功识别出处于逆转与分化阶段过渡期的细胞。此外，通过分析单细胞ChIP-Seq数据，我们鉴定出未经处理的乳腺癌细胞中一个具有类似持久性细胞表观基因组特征的亚群。