{"doi":"10.1186/s13059-024-03255-1","title":"Kernel-based testing for single-cell differential analysis","abstract":"<jats:title>Abstract</jats:title><jats:p>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.</jats:p>","journal":"Genome Biology","year":2024,"id":612389,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1576752,"name":"C. Fourneaux","orcid":null,"position":1,"is_corresponding":false},{"id":1576753,"name":"G. Durif","orcid":null,"position":2,"is_corresponding":false},{"id":1576754,"name":"P. Arsenteva","orcid":null,"position":3,"is_corresponding":false},{"id":1576755,"name":"C. Vallot","orcid":null,"position":4,"is_corresponding":false},{"id":1576756,"name":"O. Gandrillon","orcid":null,"position":5,"is_corresponding":false},{"id":1576758,"name":"S. Gonin-Giraud","orcid":null,"position":6,"is_corresponding":false},{"id":1576760,"name":"B. Michel","orcid":null,"position":7,"is_corresponding":false},{"id":1576762,"name":"F. Picard","orcid":"0000-0001-8084-5481","position":8,"is_corresponding":false},{"id":1576751,"name":"A. Ozier-Lafontaine","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Kernel-based testing for single-cell differential analysis","abstract":"<jats:title>Abstract</jats:title><jats:p>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.</jats:p>","is_dataset_classified":null,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38702740","pmcid":"PMC11069218","openalex_id":"https://openalex.org/W4396612537","authors":[],"funders":[{"funder_name":"Agence Nationale de la Recherche","grant_id":"ANR-18-CE45-0023","title":"Statistics and Machine Learning for Single Cell Genomics"},{"funder_name":"Agence Nationale de la Recherche","grant_id":"ANR-22-PESN-0002","title":null},{"funder_name":"Institut National Du Cancer","grant_id":"INCA-DGOS-INSERM-12558","title":null}],"total_grants":3,"fwci":1.6176,"citation_percentile":0.82830963,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":4},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://genomebiology.biomedcentral.com/counter/pdf/10.1186/s13059-024-03255-1","host_type":"journal"},{"url":"https://genomebiology.biomedcentral.com/counter/pdf/10.1186/s13059-024-03255-1","host_type":"publisher"},{"url":"https://link.springer.com/content/pdf/10.1186/s13059-024-03255-1.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1186/s13059-024-03255-1/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1186/s13059-024-03255-1","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38702740","host_type":"repository"},{"url":"https://hal.science/hal-04214858","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11069218","host_type":"repository"},{"url":"https://doaj.org/article/5e207c347a0b4085b0e247f809decc4d","host_type":"repository"},{"url":"https://hal.science/hal-04214858v2/document","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11069218/pdf/13059_2024_Article_3255.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11069218","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11069218?pdf=render","host_type":"Europe_PMC"},{"url":"https://dx.doi.org/10.48550/arxiv.2307.08509","host_type":""},{"url":"http://dx.doi.org/10.1186/s13059-024-03255-1","host_type":""},{"url":"http://arxiv.org/abs/2307.08509","host_type":""},{"url":"https://doi.org/10.48550/arXiv.2307.08509","host_type":""},{"url":"https://hal.science/hal-04214858v2","host_type":""},{"url":"https://doi.org/https://doi.org/10.1186/s13059-024-03255-1","host_type":""}],"fields_of_study":["Single-cell and spatial transcriptomics","Gene expression and cancer classification","Cancer Genomics and Diagnostics","0301 basic medicine","03 medical and health sciences","0303 health sciences"],"mesh_terms":["Epigenome","Breast Neoplasms","Female","Humans","Gene Expression Profiling","Epigenomics","Single-Cell Analysis","Transcriptome"],"keywords":["Epigenomics","Biology","Epigenome","Computational biology","Population","Transcriptome","Feature (linguistics)","Kernel (algebra)","Single-cell analysis","Bioinformatics","Computer science","Genetics","Cell","Gene expression","Gene","DNA methylation","Mathematics","Differential Analysis","Kernel Methods","Single Cell Transcriptomics","Single Cell Epigenomics","FOS: Computer and information sciences","Computer Science - Machine Learning","QH301-705.5","Method","Breast Neoplasms","Machine Learning (stat.ML)","QH426-470","Machine Learning (cs.LG)","Statistics - Machine Learning","Humans","Biology (General)","Gene Expression Profiling","004","620","[STAT] Statistics [stat]","[STAT]Statistics [stat]","Female"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T03:10:29.989659Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}