Kernel-Based Testing for Single-Cell Differential Analysis

Thanks to the convergence of single-cell biology and massive parallel sequencing, it is now possible to create high dimensional molecular portraits of cell populations. This technological breakthrough allows for the measurement of gene expression [34, 25, 58], chromatin states [47], and genomic variations [15] at the single-cell resolution. These advances have resulted in the production of complex high dimensional data and revolutionized our understanding of the complexity of living tissues, both in normal and pathological states. Then, the field of single-cell data science has emerged, and new methodological challenges have arisen to fully exploit the potentialities of single-cell data, among which the statistical comparison of single-cell RNA sequencing (scRNA-Seq) datasets between conditions or tissues. This step has remained a prerequisite in the process to discriminate biological from technical variabilities and to assert meaningful expression differences. While most differential analysis methods primarily focus on expression data, similar methodological challenges have arisen in the comparative analysis of single cell epigenomic datasets, based for example on single cell chromatin accessibility assays (scATAC-Seq [42]) or single cell histone modifications profiling (e.g single-cell ChIP-Seq (scChIP-seq) [19], scCUT&Tag [4]). These approaches enable the mapping of chromatin states throughout the genome and their cell-to-cell variations at an unprecedented resolution [51, 6]. These single-cell epigenomic assays offer a quantitative perspective on regulatory processes, wherein cellular heterogeneity could drive cancer progression or the development of drug resistance for instance[36]. The identification of key epigenomic features by differential analysis in disease and complex eco-systems, will be key to understand regulatory principles of gene expression and identify potential drivers of tumor progression.