PBMC Single-Cell RNA-seq Analysis

This repository provides a comprehensive analysis pipeline designed for the processing and visualization of single-cell RNA sequencing (scRNA-seq) data, specifically utilizing the Seurat package in the R programming environment. The pipeline is tailored to work with the PBMC 3K dataset, which is a widely used dataset provided by 10X Genomics. This dataset contains transcriptomic information derived from 3,000 individual Peripheral Blood Mononuclear Cells (PBMCs), offering a valuable resource for studying cellular heterogeneity within human blood. The repository includes various steps to process raw scRNA-seq data, from initial quality control to advanced visualizations, enabling users to gain insights into cellular compositions, gene expression patterns, and other key biological features at the single-cell level.

Features

• Data Loading & Preprocessing: Reads 10X Genomics PBMC data and creates a Seurat object.
• Quality Control: Filters cells based on gene expression and mitochondrial gene content.
• Normalization & Scaling: Normalizes data, finds variable features, and scales expression values.
• Dimensionality Reduction: Uses PCA and UMAP for visualization.
• Clustering & Marker Gene Identification: Finds clusters and identifies top marker genes for each cluster.
• Visualization:
  • Violin plots (VlnPlot) for quality control and marker expression.
  • UMAP projection (DimPlot) to visualize clustering.
  • Feature expression plots (FeaturePlot) for specific marker genes.

Plot Description

The included violin plots illustrate the expression distribution of key marker genes across identified cell clusters. Each violin plot shows the expression level of a gene (y-axis) across different clusters (x-axis). The width of each violin represents the density of expression values within each cluster.

Output Files

• pbmc_tutorial.rds: Processed Seurat object for further analysis.
• Plots: Generated UMAP and violin plots for cell-type identification.

Requirements

• Seurat
• ggplot2
• patchwork
• dplyr

Usage

Run the script in an R environment to reproduce the analysis:

source("pbmc_analysis.R")

Screenshots