Kernel-Based Testing for Single-Cell Differential Analysis

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.