Skip to main content
Ctrl+K

Cellucid

  • Web app
  • Python
  • R
  • Contributing
  • Changelog
  • Cellucid App
  • GitHub: cellucid
  • GitHub: cellucid-annotation
  • GitHub: cellucid-python
  • GitHub: cellucid-r
  • Web app
  • Python
  • R
  • Contributing
  • Changelog
  • Cellucid App
  • GitHub: cellucid
  • GitHub: cellucid-annotation
  • GitHub: cellucid-python
  • GitHub: cellucid-r

Section Navigation

  • Landing Pages
    • What is cellucid-python?
    • Installation
    • Compatibility matrix (must be explicit)
    • Quick start (3 levels)
  • Concepts and Mental Models (Python-side)
    • What is a “viewer” object?
    • Data flows
    • State, persistence, and scope
    • Dataset identity and reproducibility
    • Sessions → AnnData (No-Download Bridge)
    • Sessions → AnnData (Design notes)
    • Privacy, security, and offline vs online
    • Performance mental model and scaling
    • Debugging mental model (where to look first)
  • Data Preparation API (prepare/export) — The Big One
    • prepare() / export overview
    • Input requirements (global)
    • Embeddings and coordinates
    • Obs (cell metadata)
    • Var / gene metadata
    • Gene expression matrix
    • Connectivities (KNN graph)
    • Vector fields (velocity / displacement)
    • Output format specification (exports directory)
    • Performance tuning guide (prepare/export)
    • Troubleshooting (prepare/export)
  • Viewing APIs (serve / serve_anndata / show / show_anndata + loading options)
    • Viewing methods overview
    • The “14 loading options” breakdown
    • Choose your workflow (decision tree)
    • CLI: cellucid serve quickstart
    • Python: serve() and serve_anndata() quickstart
    • Jupyter: show() and show_anndata() quickstart
    • Exported directory mode (show() / serve())
    • AnnData mode (show_anndata() / serve_anndata())
    • Server mode (advanced)
    • Notebook / widget mode (advanced)
    • Viewer lifecycle, cleanup, ports, and multiple viewers
    • Remote servers, SSH tunneling, and cloud
    • Security, privacy, CORS, and networking
    • Performance, scaling, and lazy loading
    • Troubleshooting (viewing)
    • FAQ (viewing)
  • Jupyter Hooks System (Python ↔ Frontend)
    • Overview: bidirectional communication (Python ↔ viewer)
    • Quickstart: minimal round-trip (select → highlight)
    • Supported environments matrix
    • Glossary and mental model
    • Architecture: message routing (HTTP vs postMessage)
    • Python → frontend commands
    • Frontend → Python events
    • Writing robust callbacks
    • Viewer state and wait_for_event
    • Session bundles: capture from the browser (get_session_bundle)
    • Session bundles: apply to AnnData (apply_session_to_anndata)
    • Security model
    • Security: CORS, origins, and mixed content
    • Troubleshooting (hooks)
    • Screenshots and diagrams checklist (hooks)
    • Reference (hooks, commands, schemas, endpoints)
  • Examples (moved)
  • Notebooks / Tutorials (Very Detailed, Step-by-Step)
    • Start here (how to use these tutorials)
    • Beginner notebooks (wet lab friendly)
      • Open a dataset and color by clusters
      • Find marker genes and export a figure
      • Compare two groups and interpret results
    • Intermediate notebooks (computational workflows)
      • Prepare exports with quantization and compression
      • Large dataset: server mode + lazy gene expression
      • Programmatic highlighting + selection callbacks
    • Advanced notebooks (expert / developer)
      • Custom message schemas and frontend extensions
      • Session persistence and restoring analysis artifacts
      • Vector fields and velocity overlay end-to-end
    • Real-world dataset recipes (gallery)
      • 3D UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • Multi-Dimensional UMAP Export for Web Viewer
      • CellRank Pseudotime Kernel → Cellucid Vector Field Export (_test)
    • Jupyter embedding + hooks sessions (gallery)
      • Jupyter Hooks + Session → AnnData Bridge (He developmental complete)
      • Cellucid Jupyter Hooks + Sessions (HE developmental example)
  • API Reference Coverage (and the canonical API reference)
    • API Reference (cellucid-python)
      • Jupyter (notebook embedding + hooks)
      • Server (browser tab + local HTTP server)
      • Export / Data Preparation (prepare)
      • Viewer classes (Jupyter objects you control from Python)
      • Adapters (AnnData → Cellucid data model)
      • Sessions (.cellucid-session bundles)
      • Vector fields (velocity / drift overlays)
      • CLI (cellucid)
    • Public functions and classes (coverage map)
    • Error messages and exceptions (documentation patterns)
    • API reference structure and migration plan
  • Developer Docs (Python Package)
    • How to use this section
    • Codebase architecture
    • Repo layout and entry points
    • Local development setup
    • Build, install, and packaging
    • CLI architecture and commands
    • Configuration, environment variables, and logging
    • prepare() / export pipeline architecture
    • Export format spec and invariants
    • Server mode architecture, endpoints, and security
    • Jupyter embedding architecture
    • Hooks/events protocol and schema
    • Debugging playbook
    • Testing and CI
    • Release process
    • Docs development and style guide
    • Performance profiling and scaling
    • Security, privacy, and networking
    • Extension points overview
    • Extension point: add a new export feature
    • Extension point: add a new hook event or command
  • Global Troubleshooting Index (Python)
    • Installation and dependency issues
    • Data preparation issues
    • Viewer embedding issues
    • Server mode issues
    • Hooks and events issues
    • Performance and memory issues
  • Python Package Guide
  • Examples (moved)

Examples (moved)#

The example notebooks have moved into the notebooks/tutorials section:

  • Dataset preparation recipes: Real-world dataset recipes (gallery)

  • Jupyter embedding + hooks sessions: Jupyter embedding + hooks sessions (gallery)

previous

Reference (hooks, commands, schemas, endpoints)

next

Notebooks / Tutorials (Very Detailed, Step-by-Step)
Edit on GitHub

© Copyright 2026, Kemal Inecik.

Created using Sphinx 8.0.2.