Kernel-Based Testing for Single-Cell Differential Analysis

TL;DR

This paper introduces a kernel-based statistical testing framework for analyzing single-cell molecular data, enabling detection of subtle distribution differences and cell state transitions that traditional methods might miss.

Contribution

It presents a novel kernel-testing approach for non-linear comparison of single-cell distributions, applicable to gene expression and epigenomic data, revealing cell heterogeneity and state changes.

Findings

Identifies cell population heterogeneities.

Detects cell state transitions.

Uncovers subtle epigenomic variations in cancer cells.

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 profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.