Related Experiment Video
Updated: Jun 30, 2026
05:22
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue
Ching Pan Chu1, Nairuz Elazzabi1, Jules Garreau1
1Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC.
Biorxiv : the Preprint Server for Biology
|June 29, 2026
Summary
Analyzing gene coexpression across single cells (xCell) yields more reproducible and biologically relevant regulatory networks than pseudobulked (xSubject) or bulk tissue (xBulk) analyses, especially for dynamic interactions.
Area of Science:
- Genomics
- Computational Biology
- Systems Biology
Background:
- Gene coexpression analysis is crucial for inferring transcription factor regulation and building regulatory networks.
- Single-cell technologies offer new avenues for coexpression analysis, but optimal strategies remain unclear.
- Previous simulations explored differences between single-cell (xCell), pseudobulked (xSubject), and bulk (xBulk) coexpression.
Purpose of the Study:
- To validate simulation-based predictions using real-world data.
- To assess the preservation, replicability, and biological interpretability of coexpression across different analysis levels.
- To investigate the impact of analysis choices on inferring regulatory networks.
Main Methods:
- Real-world transcriptomic data from various sources were analyzed.
- Coexpression was performed at three levels: across single cells (xCell), across subjects from pseudobulked data (xSubject), and across subjects using bulk tissue (xBulk).
- Preservation across levels, replicability across independent studies, and biological interpretability were evaluated.
Main Results:
- Limited preservation of coexpression findings across different analysis levels was observed.
- Single-cell (xCell) coexpression demonstrated higher replicability across studies than pseudobulked (xSubject) analysis.
- Bulk tissue (xBulk) coexpression was heavily influenced by cellular composition variability and missed finer resolution patterns.
- xCell analysis showed the highest enrichment for known regulatory relationships.
Conclusions:
- The choice of analysis level significantly impacts coexpression outcomes and network inference.
- Single-cell-based coexpression analysis with biological replicates is recommended for inferring dynamic and replicable regulatory interactions.
- Understanding expression covariation sources is vital for accurate interpretation and replicability in regulatory network studies.
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