#PBMC Immune Cell Characterization with Single Cell Multimodal Data

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📘 Project Overview

This project analyzes peripheral blood mononuclear cells (PBMCs) from a clinical study using single-cell CITE-seq (RNA + surface protein) data. The goal is to characterize common and rare immune cell types by integrating transcriptomic and surface marker information, enabling a comprehensive view of immune cell diversity within human blood samples.

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📊 Analysis Tasks

Task 1: Data Loading, QC, and Visualization

• Created individual Seurat objects for each sample and a combined object.
• Performed quality control on RNA and ADT modalities
• Visualized key metrics (e.g., gene count, mitochondrial percent)

Task 2: Independent RNA and Protein Integration

• RNA assay: Normalization, CCA-based integration, scaling, and PCA
• Protein (ADT) assay: CLR normalization, integration, scaling, and PCA
• Addressed batch effects within each modality prior to multimodal combination

Task 3: WNN Integration, Clustering, and Annotation

• Combined RNA and protein PCA using Seurat's Weighted Nearest Neighbors (WNN)
• Generated UMAP and t-SNE plots on multimodal space
• Identified and annotated clusters using canonical immune markers

Task 4: Gene Feature Extraction and Visualization

• Extracted gene expression values for specific genes
• Added gene features to Seurat metadata
• Visualized expression patterns per cell type per Group

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💡 Notes

• Raw data is not publicly available due to client ownership and confidentiality.
• Some example outputs plots are organized by task in the output/ folder.
• This project is designed for both reproducibility and clarity.

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📬 Contact

Author: Nasim Rahmatpour Email: nasimrahmatpour1@gmail.com GitHub: (https://github.com/nasimbio)