AI in Transcriptomics Review - Website

Overview

This static website serves as an interactive companion to the comprehensive review "AI Revolution in Transcriptomics: From Single Cells to Spatial Atlases". It provides detailed reproducibility checklists for 84 scRNA-seq methods, 49 spatial transcriptomics methods, 24 foundation models, and 5 AI agents, with verified statistics and comprehensive documentation.

File Structure

├── Index.html                          # Main landing page
├── pages/
│   ├── scrna-seq-methods.html         # scRNA-seq methods page
│   ├── st-methods.html                # Spatial Transcriptomics methods page
│   ├── foundation-models.html         # Foundation Models page
│   ├── ai-agents.html                 # AI Agents page
│   └── more.html                      # Contact & more information page
├── assets/
│   ├── css/
│   │   └── style.css                  # Main stylesheet
│   ├── js/
│   │   └── main.js                    # JavaScript utilities
│   └── images/                        # Image assets
└── README.md                          # This file

Quick Start

Running Locally

Using Live Server (VS Code Extension)

• Right-click Index.html and select "Open with Live Server"
• Or use python -m http.server 8000 and navigate to http://localhost:8000

Deployment to GitHub Pages

1. Push to GitHub:

   git init
   git add .
   git commit -m "Initial commit"
   git remote add origin https://github.com/yourusername/your-repo-name.git
   git branch -M main
   git push -u origin main

2. Enable GitHub Pages:

   • Go to repository Settings → Pages
   • Source: Deploy from branch "main"
   • Folder: Root
   • Save and wait 2-3 minutes
   • Access at: https://yourusername.github.io/your-repo-name/

Website Structure

Navigation Menu

Top-Level Menu:

1. Home - Main landing page with overview and key figures
2. Task-specific Methods - Dropdown to:
   • scRNA-seq Methods (Table A)
   • ST Methods (Table B)
3. Advanced Paradigms - Dropdown to:
   • Foundation Models (Table C)
   • AI Agents (Table D)
4. More - Contact information and additional resources

Page Descriptions

Home (index.html)

• Overview of the review and its contributions
• Figure 1: Evolution timeline of AI methods (2018-2025)
• Figure 2: Tri-partite framework of AI paradigms
• Quick navigation cards linking to all major sections
• Note about table resources

scRNA-seq Methods (pages/scrna-seq-methods.html)

• Figure A1: Distribution by supervision type (vertical layout)
• Figure A2: Installation & tutorial availability (vertical layout)
• Table A: Reproducibility checklist with 84 scRNA-seq methods
• Verified Statistics:
  • Code availability: 82/84 (97.6%)
  • Installation instructions: 72/84 (85.7%)
  • Tutorials: 69/84 (82.1%)
  • Both install + tutorial: 69/84 (82.1%)
  • Unsupervised/self-supervised: 58/84 (69%)
• Key insights highlighting intrinsic pattern discovery paradigms

Spatial Transcriptomics Methods (pages/st-methods.html)

• Figure B1: Distribution by learning paradigm (vertical layout)
• Figure B2: Installation & tutorial availability (vertical layout)
• Table B: Reproducibility checklist with 49 spatial transcriptomics methods
• Verified Statistics:
  • Code availability: 49/49 (100%)
  • Installation instructions: 46/49 (93.9%)
  • Tutorials: 44/49 (89.8%)
  • Both install + tutorial: 44/49 (89.8%)
  • Unsupervised/self-supervised: 27/49 (55%)
• Application focus: spatial clustering (13 methods), cell segmentation (11 methods), deconvolution (11 methods)

Foundation Models (pages/foundation-models.html)

• Figure C: Model parameters vs training data scale (scale analysis)
• Table C: Foundation models reproducibility checklist with 24 models
• Verified Statistics:
  • Model size range: 5.2M - 27B parameters
  • Training data range: 0.575M - 116M cells
  • GPU hours range: 60 - 147,456 hours
  • Pretrained weights: 19/24 (79%)
  • pip installation: 19/24 (79.2%)
• Key innovation areas: cross-modal learning (OmiCLIP), language integration (C2S-Scale, TranscriptFormer), sequence modeling (GeneMamba)

AI Agents (pages/ai-agents.html)

• AI agent capabilities overview
• Implementation strategies comparison
• Full Table D with 5 AI agents
• Verified Statistics:
  • Code availability: 4/5 (80%)
  • Online services: 2/5 (40%)
  • Spatial transcriptomics support: 3/5 (60%)
• Typical AI agent workflow diagram

More (pages/more.html)

• Lab information and GitHub repository
• Corresponding author contact
• Contributing authors and their emails
• Citation information
• Research focus areas
• Website information and quick links

Browser Compatibility

• Chrome/Chromium (latest)
• Firefox (latest)
• Safari (latest)
• Edge (latest)
• Mobile browsers (iOS Safari, Chrome Mobile)

Contact & Support

For issues or updates to the website:

• Corresponding Author: Dr. Chao Zhang (zhangchao@mail.kiz.ac.cn)
• Tool Updates & Corrections:
  • lishixin@mail.kiz.ac.cn
  • xiaotianxiang251@mails.ucas.ac.cn
  • lanyuanyuan22@mails.ucas.ac.cn
• Lab GitHub: https://github.com/ZhangLab-Kiz

License

This website and its content are provided as supplementary material to the published review. Please cite the original paper when referencing this work.

Version History

• v1.0 (November 2025): Initial release
  • 5 main pages with comprehensive reproducibility checklists
  • 84 scRNA-seq methods with 97.6% code availability
  • 49 spatial transcriptomics methods with 100% code availability
  • 24 foundation models with detailed statistics (5.2M-27B parameters)
  • 5 AI agents with implementation strategies (80% code availability)
  • Responsive design with static figure images
  • Full navigation structure

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Last Updated: November 25, 2025