  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.
