ArticleGenome biology2024
Kernel-based testing for single-cell differential analysis.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data.Nature methods · 2026Article
- Cellular Heterogeneity and Developmental Dynamics of Aril in Papaya.International journal of molecular sciences · 2026Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Frequency and duration of sensory flicker control transcriptional profiles in 5xFAD mice.APL bioengineering · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis.BMC bioinformatics · 2025Article
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Authors and funding
9 authors.
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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.
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Registered trials
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