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Related Experiment Video

Updated: Sep 12, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in

Anderson Bussing¹, Giampiero Marra², Daping Fan³

¹Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

Statistics in Medicine

|August 7, 2025

Summary

This summary is machine-generated.

We developed scCOSMiX, a new statistical framework to analyze gene interactions in single-cell RNA sequencing (scRNA-seq) data. This method accounts for individual patient data, improving the study of dynamic gene expression changes.

Keywords:

Differential co‐expression hierarchical study design mixed effects single‐cell RNA‐seq zero‐inflated copula model

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Last Updated: Sep 12, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution gene expression data.
Understanding dynamic gene-gene interactions is crucial for cellular processes.
Existing methods struggle with the hierarchical structure of multi-subject scRNA-seq data.

Purpose of the Study:

To introduce scCOSMiX, a novel mixed-effects framework for differential co-expression analysis in scRNA-seq.
To address the challenge of cell-cell correlation within subjects in scRNA-seq data.
To enable robust modeling of dynamic gene interactions in complex experimental designs.

Main Methods:

Developed a copula-based mixed-effects framework (scCOSMiX).
Incorporated subject-level random effects to account for intra-individual cell correlations.
Modeled zero-inflation, marginal, and association parameters as functions of covariates.

Main Results:

scCOSMiX demonstrated superior performance in simulation studies compared to existing methods.
The framework effectively models conditional co-expression changes.
Applicability shown across diverse scRNA-seq protocols (droplet and plate-based).

Conclusions:

scCOSMiX provides a robust approach for analyzing dynamic gene interactions in scRNA-seq data.
The method accurately handles the hierarchical nature of multi-subject experiments.
This framework enhances the systematic study of gene-gene relationships at single-cell resolution.