https://github.com/nowon1/scrna_seq_analysis
This repository contains an analysis pipeline for processing and visualizing single-cell RNA sequencing (scRNA-seq) data using the Seurat package in R. The dataset used is the Peripheral Blood Mononuclear Cells (PBMC) 3K dataset from 10X Genomics.
https://github.com/nowon1/scrna_seq_analysis
bioinformatics cell-clustering cell-type-annotation computational-biology data-analysis-in-r differential-expression dimensionality-reduction immunology integration marker-genes multimodal-integration normalization quality-control r-package scrna-seq seurat transcriptomics-data-integration
Last synced: 4 days ago
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This repository contains an analysis pipeline for processing and visualizing single-cell RNA sequencing (scRNA-seq) data using the Seurat package in R. The dataset used is the Peripheral Blood Mononuclear Cells (PBMC) 3K dataset from 10X Genomics.
- Host: GitHub
- URL: https://github.com/nowon1/scrna_seq_analysis
- Owner: NoWon1
- Created: 2025-01-29T14:32:23.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-12T04:30:16.000Z (4 months ago)
- Last Synced: 2025-07-29T10:56:09.831Z (2 months ago)
- Topics: bioinformatics, cell-clustering, cell-type-annotation, computational-biology, data-analysis-in-r, differential-expression, dimensionality-reduction, immunology, integration, marker-genes, multimodal-integration, normalization, quality-control, r-package, scrna-seq, seurat, transcriptomics-data-integration
- Language: R
- Homepage:
- Size: 5.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```r
source("pbmc_analysis.R")
```
## Screenshots
