https://github.com/kris96tian/scrna-webapp

Interactive web application for analyzing scRNA-seq data using Scanpy
https://github.com/kris96tian/scrna-webapp

scanpy scrna-seq-analysis single-cell-rna-seq

Last synced: 5 months ago
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Interactive web application for analyzing scRNA-seq data using Scanpy

• Host: GitHub
• URL: https://github.com/kris96tian/scrna-webapp
• Owner: kris96tian
• Created: 2025-01-22T11:22:37.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-01-22T11:38:37.000Z (9 months ago)
• Last Synced: 2025-01-22T12:28:08.285Z (9 months ago)
• Topics: scanpy, scrna-seq-analysis, single-cell-rna-seq
• Language: Python
• Homepage: https://scrna-webapp.streamlit.app/
• Size: 12.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## scRNA-Scanpy-WebApp  

Visit App:  https://scrna-webapp.streamlit.app/

![image](https://github.com/user-attachments/assets/4fa04a1b-8be2-4d9d-b394-9565e02cf60d)

### Overview  

This **Streamlit**-based web application provides an interactive platform for processing and analyzing single-cell RNA sequencing (scRNA-seq) data using **Scanpy**. Workflow includes data upload, quality control (QC), normalization, dimensionality reduction, clustering, and marker discovery.

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### Features  

- **Data Upload & QC**  

  - Supports multiple formats: `10x Genomics (H5)`, `AnnData (H5AD)`, `CSV`, `MTX`, `Loom`, and Excel.  

  - Automated detection of mitochondrial, ribosomal, and hemoglobin genes for QC.  

  - Violin and scatter plots for visualizing quality metrics.

- **Processing**  

  - Doublet detection with Scrublet.  

  - Data normalization and log transformation.  

  - Highly variable gene selection (HVG).

- **Dimensionality Reduction**  

  - PCA for feature space reduction.  

  - Interactive sliders for parameter tuning.

- **Clustering & Visualization**  

  - UMAP and t-SNE integration.  

  - Leiden clustering with resolution adjustments.

- **Marker Discovery**  

  - Differential expression analysis for marker genes.  

  - Heatmaps and rank plots for visualizing key markers.

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