Kernel-based testing for single-cell differential analysis.
Genome Biol
; 25(1): 114, 2024 05 03.
Article
in En
| MEDLINE
| ID: mdl-38702740
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Single-Cell Analysis
Limits:
Female
/
Humans
Language:
En
Journal:
Genome Biol
Journal subject:
BIOLOGIA MOLECULAR
/
GENETICA
Year:
2024
Document type:
Article
Affiliation country:
France