Kernel-Based Testing for Single-Cell Differential Analysis

arXiv:2307.08509v2 Announce Type: replace-cross
Abstract: 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 …

abstract analysis arxiv challenges classifier comparison cs.lg differential distribution embedding feature framework gene global insights kernel linear non-linear population stat.ml technologies testing them type wise