RNAlytics

Project Overview

This repository contains an in-depth analysis of RNA-seq data comparing the effects of Cyclosporin A (CsA) and Voclosporin (VOC) treatments against control groups. The study utilizes iPathwayGuide to highlight differentially expressed genes (DEGs), pathway impacts, and biological processes affected by these treatments.

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Objectives

1. Analyze significant DEGs in CsA- and VOC-treated samples versus controls.
2. Explore disrupted pathways and enriched biological processes using KEGG and GO databases.
3. Highlight upstream regulators, diseases, and organ-specific signatures affected by the treatments.
4. Compare the systemic and specific effects of CsA and VOC.

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Key Comparisons

Treatment and DEGs

Attribute	CsA vs Control	VOC vs Control
Treatment	Cyclosporin A (CsA)	Voclosporin (VOC)
Number of DEGs	1,492	489
FDR Threshold	0.05	0.05
Genes Measured	Total: 15,359	Total: 15,359

Top Pathways

Pathway Name	CsA vs Control (FDR)	VOC vs Control (FDR)
Protein Processing in the ER	Significant	Most Significant (2.604e-16)
Cell Cycle	Most Significant (2.788e-17)	Significant (1.002e-6)
DNA Replication	7.860e-9	2.099e-6
Oocyte Meiosis	7.882e-4	N/A
Base Excision Repair	N/A	0.011

Gene Ontology (GO) Terms

Attribute	CsA vs Control	VOC vs Control
Total Significant Terms	1,134	1,024
Biological Processes	Chromosome organization, DNA unwinding	Chromosome organization, DNA replication
Molecular Functions	DNA helicase activity, ATP binding	Unfolded protein binding, ssDNA helicase
Cellular Components	Chromosomal regions, condensed chromosomes	Endoplasmic reticulum protein complexes

Upstream Regulators and Diseases

Attribute	CsA vs Control	VOC vs Control
Upstream Regulators	268 genes, 572 chemicals	334 genes, 598 chemicals
Disease Associations	22 diseases	35 diseases

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Key Results

Cyclosporin A (CsA)

1. Differential Gene Expression:
   • 1,492 DEGs identified at FDR 0.05.
2. Top Pathways:
   • Most significant: Cell Cycle (FDR = 2.788e-17).
3. Upstream Regulators:
   • 268 genes and 572 chemicals identified.

Voclosporin (VOC)

1. Differential Gene Expression:
   • 489 DEGs identified at FDR 0.05.
2. Top Pathways:
   • Most significant: Protein Processing in the ER (FDR = 2.604e-16).
3. Upstream Regulators:
   • 334 genes and 598 chemicals identified.

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Highlights

1. Voclosporin (VOC):
   • Targets specific pathways, particularly related to protein folding and cellular stress.
   • Unique organ- and cell-type-specific findings, including lung-related cell types.
2. Cyclosporin A (CsA):
   • Broader systemic impacts with significant effects on the cell cycle and oocyte meiosis.

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Methods

1. Data Sources:
   • KEGG pathways, Gene Ontology, BioGRID, miRNA databases, and others.
2. Analysis Techniques:
   • Differential gene expression analysis with hypergeometric distribution.
   • Pathway impact analysis combining pORA and pAcc metrics.
   • GO term pruning for high specificity.
3. Statistical Significance:
   • Adjusted p-values via FDR and Bonferroni corrections.

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Visualizations

1. Volcano Plots: Highlight up- and down-regulated genes.
2. Pathway Diagrams: Show pathway perturbation and gene-level changes.
3. Bar Plots: Rank DEGs by log fold change.

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Tech Stack

Frontend:

• Next.js: For dynamic, server-rendered pages and a robust React framework.
• Tailwind CSS: Utility-first styling framework.
• shadcn/ui: Prebuilt UI components.

Backend:

• Python: For data analysis and processing.
• Flask/FastAPI: For serving data analysis APIs.
• Pandas/NumPy: For data manipulation.
• Matplotlib/Plotly: For generating visualizations programmatically.

Data Storage:

• Raw/Processed Data:
  • Store raw data under data/raw/ and processed versions in data/processed/.
  • Include metadata about datasets in data/README.md.

Testing:

• Pytest: For backend and data processing tests.
• Jest: For testing frontend React components.

Deployment:

• Vercel: For the frontend deployment.
• AWS/Heroku/Render: For the backend deployment.

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Key Features

Data Analysis and Visualization:

• Use notebooks/ for exploratory analysis and visualizations in Python.
• Dynamic charts rendered via chart.tsx in the frontend.

API Integration:

• Serve processed data via Python APIs, like pathway_enrichment.py.

Interactive Frontend:

• Tailored components for user interaction with dynamic pathway data.

Data-Driven Testing:

• Include Python tests for APIs and data processing.

Documentation:

• Clear guidelines in the README.md for developers and contributors.

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Repository Structure

RNAlytics/
├── .git/
├── .github/
├── .wrangler/
│   └── state/
│   │   └── v3/
│   │   │   └── workflows/
├── app/
│   ├── analysis/
│   │   └── page.tsx
│   ├── api/
│   │   ├── chord/
│   │   │   └── route.ts
│   │   ├── deg-rankings/
│   │   │   └── route.ts
│   │   ├── differential-expression/
│   │   │   └── route.ts
│   │   └── pathway/
│   │   │   └── route.ts
│   ├── data/
│   │   └── page.tsx
│   ├── documentation/
│   │   └── page.tsx
│   ├── fonts/
│   │   ├── GeistMonoVF.woff
│   │   └── GeistVF.woff
│   ├── utils/
│   │   ├── chart_helpers.js
│   │   └── data_loader.js
│   ├── visualizations/
│   │   ├── layout.tsx
│   │   └── page.tsx
│   ├── favicon.ico
│   ├── globals.css
│   ├── layout.tsx
│   ├── page.tsx
│   └── providers.tsx
├── components/
│   ├── ui/
│   │   ├── accordion.tsx
│   │   ├── alert-dialog.tsx
│   │   ├── alert.tsx
│   │   ├── aspect-ratio.tsx
│   │   ├── avatar.tsx
│   │   ├── badge.tsx
│   │   ├── button.tsx
│   │   ├── calendar.tsx
│   │   ├── card.tsx
│   │   ├── chart.tsx
│   │   ├── checkbox.tsx
│   │   ├── collapsible.tsx
│   │   ├── command.tsx
│   │   ├── context-menu.tsx
│   │   ├── dialog.tsx
│   │   ├── dropdown-menu.tsx
│   │   ├── hover-card.tsx
│   │   ├── input.tsx
│   │   ├── label.tsx
│   │   ├── menubar.tsx
│   │   ├── navigation-menu.tsx
│   │   ├── popover.tsx
│   │   ├── progress.tsx
│   │   ├── radio-group.tsx
│   │   ├── scroll-area.tsx
│   │   ├── select.tsx
│   │   ├── separator.tsx
│   │   ├── sheet.tsx
│   │   ├── skeleton.tsx
│   │   ├── slider.tsx
│   │   ├── switch.tsx
│   │   ├── table.tsx
│   │   ├── tabs.tsx
│   │   ├── textarea.tsx
│   │   ├── toast.tsx
│   │   ├── toaster.tsx
│   │   ├── toggle-group.tsx
│   │   ├── toggle.tsx
│   │   ├── tooltip.tsx
│   │   └── use-toast.ts
│   ├── visualizations/
│   │   ├── ChordDiagram.tsx
│   │   ├── DegRankings.tsx
│   │   ├── PathwayDiagram.tsx
│   │   ├── PlotControls.tsx
│   │   └── VolcanoPlot.tsx
│   ├── Card.tsx
│   ├── Footer.tsx
│   ├── Header.tsx
│   ├── Hero.tsx
│   ├── MainContent.tsx
│   ├── ModeToggle.tsx
│   └── SidePanel.tsx
├── hooks/
│   ├── use-mobile.tsx
│   └── use-toast.tsx
├── lib/
├── node_modules/
├── public/
│   ├── file.svg
│   ├── globe.svg
│   ├── logo.png
│   ├── logo2.png
│   ├── logo3.svg
│   ├── next.svg
│   ├── vercel.svg
│   └── window.svg
├── tests/
│   ├── README.md
│   ├── test_api_routes.py
│   └── test_data_analysis.py
├── .DS_Store
├── .env
├── .env.example
├── .eslintrc.json
├── .gitignore
├── .npmrc
├── components.json
├── next-env.d.ts
├── next.config.mjs
├── package-lock.json
├── package.json
├── postcss.config.mjs
├── README.md
├── requirements.txt
├── server.py
├── tailwind.config.ts
├── tsconfig.json
└── wrangler.toml           

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Usage Instructions

1. Clone the repository:

   git clone https://github.com/aryehky/RNAlytics.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run analysis scripts:

   python scripts/analyze_pathways.py

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Authors

• Kayenat Aryeh: Lead Researcher and Data Analyst.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

Create and activate virtual environment

python -m venv rna_seq_env
source rna_seq_env/bin/activate 

Install backend dependencies

pip install flask pandas numpy matplotlib plotly pytest fastapi uvicorn
pip install scikit-learn scipy statsmodels
pip freeze > requirements.txt

Run the server

The frontend will be available at http://localhost:3000 The backend API will be available at http://localhost:8000 You'll need to add your actual data files to the data/raw directory Create Jupyter notebooks in the notebooks directory for your analysis Add your tests in the tests directory Configure your deployment settings based on your chosen platforms (Vercel, AWS, etc.)