Yang, Shuyi;
Bussing, Anderson;
Marra, Giampiero;
Brinkmeier, Michelle L;
Camper, Sally A;
Davis, Shannon W;
Ho, Yen-Yi;
(2025)
Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.
BMC Bioinformatics
, 26
(1)
, Article 199. 10.1186/s12859-025-06218-w.
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Abstract
Background: The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. 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. Results: 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 and wild-type mice. Conclusions: 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.
| Type: | Article |
|---|---|
| Title: | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12859-025-06218-w |
| Publisher version: | https://doi.org/10.1186/s12859-025-06218-w |
| Language: | English |
| Additional information: | © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Biotechnology & Applied Microbiology, Mathematical & Computational Biology, Biochemistry & Molecular Biology, Zero-inflated bivariate count data, Single-cell RNA sequencing, Dynamic correlation, Pseudotime, Non-linear regression, Semiparametric regression, Covariate-dependent correlation structure, ADDITIVE-MODELS, LOCATION, LINEAGE, SCALE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214566 |
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