{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:04:50Z","timestamp":1783919090898,"version":"3.55.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2023-146833OB-I00"],"award-info":[{"award-number":["PID2023-146833OB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Differential gene expression analysis is essential for characterizing immune cell phenotypes, yet conventional approaches\u2014typically based on log\n                      <jats:sub>2<\/jats:sub>\n                      fold\u2212change (log\n                      <jats:sub>2<\/jats:sub>\n                      FC) and False Discovery Rate (FDR) thresholds\u2014often struggle to capture the complexity and continuum of transcriptional states.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address this limitation, we developed a new computational method for gene selection from mRNA\u2212seq data: the Cartesian Distance\u2212Based Gene Expression (CDBGE) selector. This algorithm identifies differentially expressed genes by leveraging multidimensional expression distances rather than relying on traditional univariate statistical cutoffs, enabling a more refined and biologically coherent gene\u2212marker selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We applied the CDBGE selector to construct a gene\u2212based framework for distinguishing macrophage polarization states. The model was trained using publicly available macrophage transcriptomic datasets and subsequently validated with\n                      <jats:italic>in vitro<\/jats:italic>\n                      human macrophages stimulated with IFN\u2212\u03b3\/LPS, conditioned medium from HepG2 liver cancer cells (Sec-HepG2), or IL10. To evaluate its generalizability beyond macrophage biology, we further tested the method on human embryonic stem cell differentiation datasets. Compared with standard differential expression pipelines, the CDBGE selector more effectively identified subtype\u2212specific markers and revealed dynamic transcriptional transitions over time.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings demonstrate that distance\u2212based gene selection provides an improved strategy for analyzing complex mRNA\u2212seq datasets. Overall, the CDBGE selector offers a robust, scalable, and broadly applicable tool for differential gene expression analysis and phenotype characterization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1843796","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T04:23:30Z","timestamp":1783916610000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A versatile distance-based approach for gene expression selection across diverse biological systems"],"prefix":"10.3389","volume":"17","author":[{"given":"Qiaoling","family":"Ye","sequence":"first","affiliation":[{"name":"Innate Immunity Group, Germans Trias i Pujol Research Institute (IGTP)","place":["Badalona, Spain"]},{"name":"Departament de F\u00edsica, Institute for Research and Innovation in Health (IRIS), Universitat Polit\u00e8cnica de Catalunya \u2013 BarcelonaTech","place":["Castelldefels, 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