Kernel-based testing for single-cell differential analysis

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...

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Bibliographic Details
Published in:Genome Biology 2024-05, Vol.25 (1), p.114-21, Article 114
Main Authors: Ozier-Lafontaine, A, Fourneaux, C, Durif, G, Arsenteva, P, Vallot, C, Gandrillon, O, Gonin-Giraud, S, Michel, B, Picard, F
Format: Article
Language:English
Subjects:
Breast Neoplasms - genetics
Cancer
Comparative analysis
Data science
Datasets
Differential analysis
Discriminant analysis
Embedding
Epigenome
Epigenomics - methods
Female
Gene expression
Gene Expression Profiling - methods
Generalized linear models
Humans
Hypotheses
Hypothesis testing
Kernel methods
Methods
Power
Single cell epigenomics
Single cell transcriptomics
Single-Cell Analysis - methods
Transcriptome
Transcriptomes
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Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-024-03255-1