Analysis of COVID-19 Patient PBMCs Using Single-cell Multiome Profiling

This repository contains analysis scripts for the single-cell multi-omics profiling (transcriptomic and epigenomic) of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients. The analysis pipeline, primarily implemented in R using Seurat and Signac, includes data preprocessing, quality control, multimodal dimensionality reduction, clustering, cell type annotation, differential gene expression and chromatin accessibility analysis, and transcription factor (TF) motif enrichment.

To further explore transcriptional regulatory programs, SCENIC+ in Python was used to infer TF activity and regulatory network perturbations across immune cell types. This integrative approach aims to elucidate cell-type-specific regulatory mechanisms altered by SARS-CoV-2 infection and to identify potential biomarkers or therapeutic targets relevant to immune response and disease progression.