Bussing, Anderson;
Marra, Giampiero;
Fan, Daping;
Shinohara, Russell;
Tu, Danni;
Ho, Yen-Yi;
(2025)
scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.
STATISTICS IN MEDICINE
, 44
(18-19)
, Article e70213. 10.1002/sim.70213.
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Abstract
Advancements in single-cell RNA-sequencing (scRNA-seq) technologies generate a wealth of gene expression data that provide exciting opportunities for studying gene-gene interactions systematically at individual cell resolution. Genetic interactions within a cell are tightly regulated and often highly dynamic in response to internal cellular signals and external stimuli. Evidence of these dynamic interactions can often be observed in scRNA-seq data by examining conditional co-expression changes. Existing approaches for studying these dynamic interaction changes in scRNA-seq data do not address the multi-subject hierarchical design commonly considered in single-cell experiments. In this paper, we propose a Mixed-effects framework for differential Coexpression and transcriptional interaction modeling in Single-Cell RNA-seq (scCOSMiX) to account for the cell-cell correlation from the same individual. The proposed copula-based approach allows the zero-inflation, marginal, and association parameters to be modeled as functions of covariates with subject-level random effects, to enable analyses to be tailored to the data under consideration. A series of simulation analyses were conducted to evaluate and compare the performance of scCOSMiX to other existing approaches. We applied the proposed method to both droplet and plate-based scRNA-seq data sets GSE266919 and GSE108989 to illustrate its applicability across distinct scRNA-seq experimental protocols.
| Type: | Article |
|---|---|
| Title: | scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/sim.70213 |
| Publisher version: | https://doi.org/10.1002/sim.70213 |
| Language: | English |
| Additional information: | © 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Mathematical & Computational Biology, Public, Environmental & Occupational Health, Medical Informatics, Medicine, Research & Experimental, Statistics & Probability, Research & Experimental Medicine, Mathematics, Differential co-expression, hierarchical study design, mixed effects, single-cell RNA-seq, zero-inflated copula model, ASSOCIATION, SELECTION, NETWORKS |
| 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/10214565 |
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