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Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to miss meaningful genetic interactions. Gene co-expression analysis addresses this limitation by identifying coordinated changes in gene expression in response to cellular conditions, such as developmental or temporal trajectories. Existing approaches to gene co-expression analysis often assume restrictive linear relationships. However, gene co-expression can change in complex, non-linear ways, which suggests the need for more flexible and accurate methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We propose a copula-based framework, TIME-CoExpress, with proper data-driven smoothing functions to model non-linear changes in gene co-expression along cellular temporal trajectories. Our method provides the flexibility to incorporate characteristics commonly observed in scRNAseq data, such as over-dispersion and zero-inflation, into the modeling framework. In addition to modeling gene co-expression, TIME-CoExpress captures dynamic changes in gene-level zero-inflation rates and mean expression levels, providing a more comprehensive analysis of scRNAseq data. Through a series of simulation analyses, we evaluated the performance of the proposed approach. We further demonstrated its implementation using a scRNAseq dataset and identified differentially co-expressed gene pairs along the cellular temporal trajectory during pituitary embryonic development, comparing\n                      <jats:inline-formula>\n                        <jats:tex-math>$${Nxn}^{-\/-}$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      and wild-type mice.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed framework enables flexible and robust identification of dynamic, non-linear changes in gene co-expression, zero-inflation rates, and mean expression levels along temporal trajectories in scRNAseq data. Detecting these changes provides deeper insights into the biological processes and offers a better understanding of gene regulation throughout cellular development.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06218-w","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T14:58:14Z","timestamp":1753801094000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data"],"prefix":"10.1186","volume":"26","author":[{"given":"Shuyi","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anderson","family":"Bussing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giampiero","family":"Marra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle L.","family":"Brinkmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally A.","family":"Camper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shannon W.","family":"Davis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Yi","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"issue":"8","key":"6218_CR1","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1038\/s41592-020-0885-x","volume":"17","author":"S Ghazanfar","year":"2020","unstructured":"Ghazanfar S, Lin Y, Su X, Lin DM, Patrick E, Han Z-G, Marioni JC, Yang J. 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