{"id":24795140,"url":"https://github.com/nowon1/scrna_seq_analysis","last_synced_at":"2025-10-04T07:44:07.500Z","repository":{"id":274816043,"uuid":"924165488","full_name":"NoWon1/scRNA_seq_analysis","owner":"NoWon1","description":"This repository contains an analysis pipeline for processing and visualizing single-cell RNA sequencing (scRNA-seq) data using the Seurat package in R. 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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.\n\n## Features\n- Data Loading \u0026 Preprocessing: Reads 10X Genomics PBMC data and creates a Seurat object.\n- Quality Control: Filters cells based on gene expression and mitochondrial gene content.\n- Normalization \u0026 Scaling: Normalizes data, finds variable features, and scales expression values.\n- Dimensionality Reduction: Uses PCA and UMAP for visualization.\n- Clustering \u0026 Marker Gene Identification: Finds clusters and identifies top marker genes for each cluster.\n- Visualization:\n   - Violin plots (VlnPlot) for quality control and marker expression.\n   - UMAP projection (DimPlot) to visualize clustering.\n   - Feature expression plots (FeaturePlot) for specific marker genes.\n\n## Plot Description\nThe 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.\n\n## Output Files\n\n- pbmc_tutorial.rds: Processed Seurat object for further analysis.\n- Plots: Generated UMAP and violin plots for cell-type identification.\n\n## Requirements\n\n- Seurat\n- ggplot2\n- patchwork\n- dplyr\n\n## Usage\nRun the script in an R environment to reproduce the analysis:\n\n```r\nsource(\"pbmc_analysis.R\")\n```\n\n## Screenshots\n![1](https://github.com/user-attachments/assets/47abdb4b-e26a-4985-8519-90d2fbba0f66)\n![2](https://github.com/user-attachments/assets/a6b1dbab-3c15-4a21-8293-00fa9bc34b66)\n![3](https://github.com/user-attachments/assets/5d837ba6-8ea8-44e6-9354-89bbf6370c96)\n![4](https://github.com/user-attachments/assets/74b86de1-3a00-48bd-9189-0697fc72e42e)\n![5](https://github.com/user-attachments/assets/77f6b3dc-8187-4809-a59b-307cf75dbad9)\n![6](https://github.com/user-attachments/assets/82d9372b-c0a1-47a5-b97b-f906adfbd904)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnowon1%2Fscrna_seq_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnowon1%2Fscrna_seq_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnowon1%2Fscrna_seq_analysis/lists"}