Gene regulatory networks define human airway epithelial cell types and their distinct responses to Type I interferon
Codebases and analysis pipelines supporting the manuscript:
Gene regulatory networks define human airway epithelial cell types and their distinct responses to Type I interferon
Bejjani et al. (2026) bioRxiv. DOI: https://doi.org/10.64898/2026.05.09.724010
Explore the GRN and data generated in this study, using our Cytoscape sessions and hosted tracks:
- Cytoscape GRN Visualization: Download sessions for interactive visualization of gene regulatory networks using Cytoscape.
- UCSC Genome Browser Track Hub: Visualize steady-state and interferon-responsive accessible chromatin and in silico maxATAC TF binding site predictions, resolved by cell populations and timepoints.
The analysis is broken down into modular codebases. Detailed instructions for running the code within each module can be found in their respective directories.
- scRNA-seq de-multiplexing and cell type annotation - PENDING - Initial scRNA-seq workflow, including de-multiplexing samples, Seurat object creation and cell type annotation.
- Cell type-resolved IRG identification - Differential gene expression analysis to identify IFN-responsive genes for each cell type.
- Cell type-resolved IFN-responsive peak identification - Differential gene expression analysis to identify IFN-responsive peaks for each cell type.
- TFBS enrichment in IFN-responsive chromatin - Simulation-based TFBS enrichment analysis of IFN-increased chromatin regions, accounting for cell type-specific steady-state accessibility.
- Enrichment analyses using Fisher's exact test or GSEA - Perform the various enrichment analyses using Fisher's exact tests of GSEA.
Data generated in this manuscript, including Seurat objects, have been deposited on the Gene Expression Ombinus (GEO) and will be made publicly available following the study's publication:
- maxATAC GitHub Repository: in silico TF binding site predictions using maxATAC.
- Inferelator Github Repository: GRN inference was performed using the Inferelator.
- TF-TF Module Analysis Github Repository: Codebase used for TF-TF module analysis (see
example_Th17_tfTfModules.m). - Out-of-Sample Gene Expression Prediction Github Repository: Codebase used for out-of-sample gene expression prediction to determine model complexity (average number of TF regulators per gene, see
example_workflow_Th17_r2Pred.m).
The analyses in this study were performed using R [4.2.0] and [4.2.2].