Dissecting the epigenome dynamics in human immune cells upon viral and chemical exposure by multimodal single-cell profiling

Supporting repository for the manuscript of the same name.

📁 Project Directory Structure

figures/              # Output figures from the analysis
sample_annots/        # Sample-level annotations and metadata
data/                 # methylTFR objects
src/
├── atac/             # scATAC-seq analysis scripts
├── meth/             # snmC-seq analysis scripts
├── integration/      # scATAC-seq + snmC-seq integrative analysis scripts

utils/                # Utility functions shared across pipelines

📦 Dependencies

Package	Description
R ≥ 4.1	Minimum required R version
RnBeads	DNA methylation analysis
ChrAccR	Chromatin accessibility analysis
ArchR	Single-cell ATAC-seq analysis framework
dplyr	Data manipulation and transformation
data.table	Data manipulation and transformation
methylTFR	Methylation based TF activities
chromVAR	Accessibility based TF activities
ggplot2	Data visualization
ComplexHeatmap	Complex heatmaps with annotations

🧬 Getting Started

We separated the analysis into three main categories:

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1. 🔓 ATAC: Chromatin Accessibility Analysis

This section includes single-cell and pseudobulk-based ATAC-seq analysis. The workflow is modular and organized into the following steps:

Step	Script	Description
01	01_quality_control.R	Perform quality control on raw scATAC data
02	02_cluster_and_batch.R	Handle clustering and batch correction
03	03_annotate.R	Annotate cell-types
04.1	04_1_markers.R	Plot cell type markers
04.2	04_2_cellprops.R	Analyze cell-type proportions across exposures
05	05_pseudobulk.R	Perform pseudobulk aggregation per cell-type
07.1	07_1_run_ChrAccR.R	Run ChrAccR analysis
07.2	07_2_run_ChrAccR_C19.R	Run ChrAccR analysis focused on COVID-19 samples
08.1	08_1_chraccR_plots.R	Generate visualizations from ChrAccR outputs
08.2	08_2_C19_trackplots.R	Create genome track plots for COVID-19 differential peaks in CD14+ Monocytes
09	09_gene_exp_vs_activity.R	Correlate gene expression with chromatin accessibility in CD14+ Monocytes for protein coding genes
10	10_tcells.R	T-cell subset analysis for longitudinal HIV cohort

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2. 🧬 METH: Methylation Analysis

This section includes pseudobulk methylation analysis focused on ATAC peaks.

Step	Script	Description
01	01_meth_pseudobulks.R	Create pseudobulks for methylation data
02.1	02_1_run_RnBeads.R	Run RnBeads analysis using the pseudobulks
02.2	02_2_run_RnBeads_C19.R	Run RnBeads analysis for COVID-19 monocytes using the pseudobulks
03	03_RnBeads_plots.R	Generate visualizations from RnBeads outputs
04	04_C19_pseudobulks.R	Generate pseudobulk per condition in C19 for mTFR visualizations
05	05_C19_mfoot.R	Generate motif footprint plots for C19
06	06_C19_mTFR.R	Run methylTFR algorithm to create deviation scores for C19
07	07_C19_mTFR_plots.R	Generate visualizations methylTFR deviation scores for C19

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3. 🧩 Integration

This section includes integration of single-cell methylation and chromatin accessibility data from overlapping samples, based on shared ATAC peaks.

Step	Script	Description
01	01_prepare_sampleannot.R	Format sample annotation ready for aggregation
02	02_aggregate_meth.R	Aggregate scMeth over peak regions
03	03_quality_check.R	Perform quality control on aggregated data
04	04_lsi.R	Apply Latent Semantic Indexing (LSI) for dimensionality reduction
05	05_cca.R	Run Canonical Correlation Analysis (CCA) for multi-omic alignment
06	06_plot_cca.R	Visualize results of CCA
07	07_mTFR_run.R	Run methylTFR algorithm to create deviation scores
08	08_cor_analysis.R	Correlation analysis of mTFR and cVAR matricies
09	09_mfoot.R	Generate motif footprint plots for T-cells
10	10_zdiff.R	Z-score difference plots for T-cells

📫 Contact

For questions or contributions, feel free to reach out to the maintainer.