{"id":21205899,"url":"https://github.com/aryehky/rnalytics","last_synced_at":"2025-07-13T20:34:59.856Z","repository":{"id":293930545,"uuid":"985532943","full_name":"aryehky/RNAlytics","owner":"aryehky","description":"🔬 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…","archived":false,"fork":false,"pushed_at":"2025-05-18T01:34:04.000Z","size":516,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-18T02:40:40.363Z","etag":null,"topics":["flask","matplotlib","nextjs","pandas","python","react","tailwind"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aryehky.png","metadata":{},"created_at":"2025-05-18T01:04:46.000Z","updated_at":"2025-05-18T01:35:55.000Z","dependencies_parsed_at":null,"dependency_job_id":"63819dc2-83c9-465e-8758-bdc1441b9246","html_url":"https://github.com/aryehky/RNAlytics","commit_stats":null,"previous_names":["aryehky/rna_lytics"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/aryehky/RNAlytics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aryehky%2FRNAlytics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aryehky%2FRNAlytics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aryehky%2FRNAlytics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aryehky%2FRNAlytics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aryehky","download_url":"https://codeload.github.com/aryehky/RNAlytics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aryehky%2FRNAlytics/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264551654,"owners_count":23626536,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["flask","matplotlib","nextjs","pandas","python","react","tailwind"],"created_at":"2024-11-20T20:53:35.367Z","updated_at":"2025-07-13T20:34:59.841Z","avatar_url":"https://github.com/aryehky.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# RNAlytics\n\n## Project Overview\nThis 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\n\n---\n\n## Objectives\n1. Analyze significant DEGs in CsA- and VOC-treated samples versus controls.\n2. Explore disrupted pathways and enriched biological processes using KEGG and GO databases.\n3. Highlight upstream regulators, diseases, and organ-specific signatures affected by the treatments.\n4. Compare the systemic and specific effects of CsA and VOC.\n\n---\n\n## Key Comparisons\n\n### **Treatment and DEGs**\n| Attribute        | CsA vs Control         | VOC vs Control         |\n|------------------|------------------------|------------------------|\n| **Treatment**    | Cyclosporin A (CsA)    | Voclosporin (VOC)      |\n| **Number of DEGs** | 1,492                | 489                    |\n| **FDR Threshold** | 0.05                 | 0.05                   |\n| **Genes Measured** | Total: 15,359        | Total: 15,359          |\n\n### **Top Pathways**\n| Pathway Name                      | CsA vs Control (FDR)    | VOC vs Control (FDR)    |\n|-----------------------------------|-------------------------|-------------------------|\n| **Protein Processing in the ER**  | Significant             | **Most Significant (2.604e-16)** |\n| **Cell Cycle**                    | **Most Significant (2.788e-17)** | Significant (1.002e-6) |\n| **DNA Replication**               | 7.860e-9               | 2.099e-6               |\n| **Oocyte Meiosis**                | 7.882e-4               | N/A                    |\n| **Base Excision Repair**          | N/A                    | 0.011                  |\n\n### **Gene Ontology (GO) Terms**\n| Attribute                | CsA vs Control                        | VOC vs Control                        |\n|--------------------------|---------------------------------------|---------------------------------------|\n| **Total Significant Terms** | 1,134                              | 1,024                                 |\n| **Biological Processes** | Chromosome organization, DNA unwinding | Chromosome organization, DNA replication |\n| **Molecular Functions**  | DNA helicase activity, ATP binding    | Unfolded protein binding, ssDNA helicase |\n| **Cellular Components**  | Chromosomal regions, condensed chromosomes | Endoplasmic reticulum protein complexes |\n\n### **Upstream Regulators and Diseases**\n| Attribute              | CsA vs Control             | VOC vs Control             |\n|------------------------|----------------------------|----------------------------|\n| **Upstream Regulators** | 268 genes, 572 chemicals   | 334 genes, 598 chemicals   |\n| **Disease Associations** | 22 diseases               | 35 diseases                |\n\n---\n\n## Key Results\n\n### Cyclosporin A (CsA)\n1. **Differential Gene Expression**:\n   - 1,492 DEGs identified at FDR 0.05.\n2. **Top Pathways**:\n   - Most significant: *Cell Cycle* (FDR = 2.788e-17).\n3. **Upstream Regulators**:\n   - 268 genes and 572 chemicals identified.\n\n### Voclosporin (VOC)\n1. **Differential Gene Expression**:\n   - 489 DEGs identified at FDR 0.05.\n2. **Top Pathways**:\n   - Most significant: *Protein Processing in the ER* (FDR = 2.604e-16).\n3. **Upstream Regulators**:\n   - 334 genes and 598 chemicals identified.\n\n---\n\n## Highlights\n1. **Voclosporin (VOC)**:\n   - Targets specific pathways, particularly related to protein folding and cellular stress.\n   - Unique organ- and cell-type-specific findings, including lung-related cell types.\n2. **Cyclosporin A (CsA)**:\n   - Broader systemic impacts with significant effects on the cell cycle and oocyte meiosis.\n\n---\n\n## Methods\n1. **Data Sources**:\n   - KEGG pathways, Gene Ontology, BioGRID, miRNA databases, and others.\n2. **Analysis Techniques**:\n   - Differential gene expression analysis with hypergeometric distribution.\n   - Pathway impact analysis combining pORA and pAcc metrics.\n   - GO term pruning for high specificity.\n3. **Statistical Significance**:\n   - Adjusted p-values via FDR and Bonferroni corrections.\n\n---\n\n## Visualizations\n1. **Volcano Plots**: Highlight up- and down-regulated genes.\n2. **Pathway Diagrams**: Show pathway perturbation and gene-level changes.\n3. **Bar Plots**: Rank DEGs by log fold change.\n\n---\n\n---\n## Tech Stack\n\n### Frontend:\n- **Next.js**: For dynamic, server-rendered pages and a robust React framework.\n- **Tailwind CSS**: Utility-first styling framework.\n- **shadcn/ui**: Prebuilt UI components.\n\n### Backend:\n- **Python**: For data analysis and processing.\n- **Flask/FastAPI**: For serving data analysis APIs.\n- **Pandas/NumPy**: For data manipulation.\n- **Matplotlib/Plotly**: For generating visualizations programmatically.\n\n### Data Storage:\n- **Raw/Processed Data**:\n  - Store raw data under `data/raw/` and processed versions in `data/processed/`.\n  - Include metadata about datasets in `data/README.md`.\n\n### Testing:\n- **Pytest**: For backend and data processing tests.\n- **Jest**: For testing frontend React components.\n\n### Deployment:\n- **Vercel**: For the frontend deployment.\n- **AWS/Heroku/Render**: For the backend deployment.\n\n---\n\n## Key Features\n\n### Data Analysis and Visualization:\n- Use `notebooks/` for exploratory analysis and visualizations in Python.\n- Dynamic charts rendered via `chart.tsx` in the frontend.\n\n### API Integration:\n- Serve processed data via Python APIs, like `pathway_enrichment.py`.\n\n### Interactive Frontend:\n- Tailored components for user interaction with dynamic pathway data.\n\n### Data-Driven Testing:\n- Include Python tests for APIs and data processing.\n\n### Documentation:\n- Clear guidelines in the `README.md` for developers and contributors.\n\n---\n\n## Repository Structure\n```\nRNAlytics/\n├── .git/\n├── .github/\n├── .wrangler/\n│   └── state/\n│   │   └── v3/\n│   │   │   └── workflows/\n├── app/\n│   ├── analysis/\n│   │   └── page.tsx\n│   ├── api/\n│   │   ├── chord/\n│   │   │   └── route.ts\n│   │   ├── deg-rankings/\n│   │   │   └── route.ts\n│   │   ├── differential-expression/\n│   │   │   └── route.ts\n│   │   └── pathway/\n│   │   │   └── route.ts\n│   ├── data/\n│   │   └── page.tsx\n│   ├── documentation/\n│   │   └── page.tsx\n│   ├── fonts/\n│   │   ├── GeistMonoVF.woff\n│   │   └── GeistVF.woff\n│   ├── utils/\n│   │   ├── chart_helpers.js\n│   │   └── data_loader.js\n│   ├── visualizations/\n│   │   ├── layout.tsx\n│   │   └── page.tsx\n│   ├── favicon.ico\n│   ├── globals.css\n│   ├── layout.tsx\n│   ├── page.tsx\n│   └── providers.tsx\n├── components/\n│   ├── ui/\n│   │   ├── accordion.tsx\n│   │   ├── alert-dialog.tsx\n│   │   ├── alert.tsx\n│   │   ├── aspect-ratio.tsx\n│   │   ├── avatar.tsx\n│   │   ├── badge.tsx\n│   │   ├── button.tsx\n│   │   ├── calendar.tsx\n│   │   ├── card.tsx\n│   │   ├── chart.tsx\n│   │   ├── checkbox.tsx\n│   │   ├── collapsible.tsx\n│   │   ├── command.tsx\n│   │   ├── context-menu.tsx\n│   │   ├── dialog.tsx\n│   │   ├── dropdown-menu.tsx\n│   │   ├── hover-card.tsx\n│   │   ├── input.tsx\n│   │   ├── label.tsx\n│   │   ├── menubar.tsx\n│   │   ├── navigation-menu.tsx\n│   │   ├── popover.tsx\n│   │   ├── progress.tsx\n│   │   ├── radio-group.tsx\n│   │   ├── scroll-area.tsx\n│   │   ├── select.tsx\n│   │   ├── separator.tsx\n│   │   ├── sheet.tsx\n│   │   ├── skeleton.tsx\n│   │   ├── slider.tsx\n│   │   ├── switch.tsx\n│   │   ├── table.tsx\n│   │   ├── tabs.tsx\n│   │   ├── textarea.tsx\n│   │   ├── toast.tsx\n│   │   ├── toaster.tsx\n│   │   ├── toggle-group.tsx\n│   │   ├── toggle.tsx\n│   │   ├── tooltip.tsx\n│   │   └── use-toast.ts\n│   ├── visualizations/\n│   │   ├── ChordDiagram.tsx\n│   │   ├── DegRankings.tsx\n│   │   ├── PathwayDiagram.tsx\n│   │   ├── PlotControls.tsx\n│   │   └── VolcanoPlot.tsx\n│   ├── Card.tsx\n│   ├── Footer.tsx\n│   ├── Header.tsx\n│   ├── Hero.tsx\n│   ├── MainContent.tsx\n│   ├── ModeToggle.tsx\n│   └── SidePanel.tsx\n├── hooks/\n│   ├── use-mobile.tsx\n│   └── use-toast.tsx\n├── lib/\n├── node_modules/\n├── public/\n│   ├── file.svg\n│   ├── globe.svg\n│   ├── logo.png\n│   ├── logo2.png\n│   ├── logo3.svg\n│   ├── next.svg\n│   ├── vercel.svg\n│   └── window.svg\n├── tests/\n│   ├── README.md\n│   ├── test_api_routes.py\n│   └── test_data_analysis.py\n├── .DS_Store\n├── .env\n├── .env.example\n├── .eslintrc.json\n├── .gitignore\n├── .npmrc\n├── components.json\n├── next-env.d.ts\n├── next.config.mjs\n├── package-lock.json\n├── package.json\n├── postcss.config.mjs\n├── README.md\n├── requirements.txt\n├── server.py\n├── tailwind.config.ts\n├── tsconfig.json\n└── wrangler.toml           \n```\n\n---\n\n## Usage Instructions\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/aryehky/RNAlytics.git\n   ```\n2. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n3. Run analysis scripts:\n   ```bash\n   python scripts/analyze_pathways.py\n   ```\n\n---\n\n## Authors\n- **Kayenat Aryeh**: Lead Researcher and Data Analyst.\n\n---\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n\n# Create and activate virtual environment\n```\npython -m venv rna_seq_env\nsource rna_seq_env/bin/activate \n```\n\n# Install backend dependencies\n```\npip install flask pandas numpy matplotlib plotly pytest fastapi uvicorn\npip install scikit-learn scipy statsmodels\npip freeze \u003e requirements.txt\n```\n\n# Run the server\nThe frontend will be available at http://localhost:3000\nThe backend API will be available at http://localhost:8000\nYou'll need to add your actual data files to the data/raw directory\nCreate Jupyter notebooks in the notebooks directory for your analysis\nAdd your tests in the tests directory\nConfigure your deployment settings based on your chosen platforms (Vercel, AWS, etc.)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryehky%2Frnalytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryehky%2Frnalytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryehky%2Frnalytics/lists"}