{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:35Z","timestamp":1772138075575,"version":"3.50.1"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013678","name":"University of Colorado Cancer Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013678","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>High-throughput sequencing of single-cell data can be used to rigorously evaluate cell specification and enable intricate variations between groups or conditions to be identified. Many popular existing methods for differential expression target differences in aggregate measurement (mean, median, sum) and limit their approaches to detect only global differential changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. CytoKernel is specifically designed to assess the differential expression of single-cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single-cell data, by calculating the pairwise divergence\/distance between distributions of subjects. It can detect both patterns involving changes in the aggregate, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single-cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the false discovery rate and shows favorable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http:\/\/bioconductor.org\/packages\/cytoKernel.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf399","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T14:00:10Z","timestamp":1752501610000},"source":"Crossref","is-referenced-by-count":1,"title":["cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7537-6374","authenticated-orcid":false,"given":"Tusharkanti","family":"Ghosh","sequence":"first","affiliation":[{"name":"Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus , Aurora, CO 80045,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2235-6193","authenticated-orcid":false,"given":"Ryan M","family":"Baxter","sequence":"additional","affiliation":[{"name":"Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus , Aurora, CO 80045,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3268-610X","authenticated-orcid":false,"given":"Souvik","family":"Seal","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina , Charleston, SC 29425,","place":["United States"]}]},{"given":"Victor G","family":"Lui","sequence":"additional","affiliation":[{"name":"Center for Translational Immunology, Benaroya Research Institute at Virginia Mason , Seattle, WA 98101,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1089-7283","authenticated-orcid":false,"given":"Pratyaydipta","family":"Rudra","sequence":"additional","affiliation":[{"name":"Department of Statistics, Oklahoma State University , Stillwater, OK 74078,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5252-0006","authenticated-orcid":false,"given":"Thao","family":"Vu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus , Aurora, CO 80045,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3969-6597","authenticated-orcid":false,"given":"Elena W Y","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus , Aurora, CO 80045,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6618-1316","authenticated-orcid":false,"given":"Debashis","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus , Aurora, CO 80045,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"2025073113314856200_btaf399-B1","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1016\/j.immuni.2020.11.016","article-title":"Low-avidity CD4+ T cell responses to SARS-CoV-2 in unexposed individuals and humans with severe 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