Kernel-based testing for single-cell differential analysis.
Genome Biol
; 25(1): 114, 2024 05 03.
Article
en 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Análisis de la Célula Individual
Límite:
Female
/
Humans
Idioma:
En
Revista:
Genome Biol
/
Genome biol
/
Genome biology (Online)
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia