Cellucid

See every cell. Keep the biology in view.

Cellucid is a GPU-accelerated, browser-first workspace for exploring single-cell data interactively. Move through 1D, 2D, and 3D embeddings; color by genes or cell metadata; filter and compare populations; inspect the neighbor graph and velocity overlays; and carry the result into a figure, notebook, saved session, or collaborative annotation round. Every term the app uses is defined in the UI glossary.

Open Cellucid

🚀 Take the 60-second tour

The Suo atlas makes Cellucid’s scale tangible: hundreds of thousands of cells remain individually visible as you move from tissue-wide structure into a close-up view.

Start with the question in front of you

You do not need to learn the whole application before doing useful work. Pick the route that matches today’s question.

Where are my populations?

Colour by a categorical obs field or a marker gene, then filter the noise away and highlight a population without losing the surrounding context.

Fields, Coloring, and Legends (Obs/Var)

Where are cells going?

Open the real Pancreas sample, compare its 1D, 2D, and 3D embeddings, and inspect the dimension-matched RNA-velocity overlay.

Standard Pancreas sample dataset

How do I share my own data?

Build a small public catalog with stable dataset identities, test it locally, and share an exact URL that opens the intended dataset.

Publish a Custom Dataset Repository

Can the viewer stay beside my analysis?

Embed Cellucid in Jupyter, inspect connection health, exchange selections and state with Python, and stop the local viewer cleanly.

Jupyter Integration (Notebook Embedding)

Bring data in the way that fits the work

What you have	Best first path	Why
A prepared Cellucid folder	Browser Prepared picker	Fast local exploration with no separate server
An H5AD under 512 MiB, or a portable Zarr ZIP	Browser file picker	One direct local selection, no Python installed
A large H5AD, Zarr directory, or prepared catalog	Python server	Exact server-side validation and on-demand browser gene requests
An in-memory AnnData object	Jupyter integration	A tight analysis-to-visualization loop
A public collection of prepared datasets	Custom repository guide	No running server; exact catalog and share links
A static host or CDN you already control	Self-hosted exports root	?exportsBaseUrl= fills the app’s own Sample datasets: dropdown from your catalog

The complete data-loading map explains the memory, network, privacy, and reproducibility tradeoffs before you commit to a path.

Learn at your own depth

Web app

Warm, task-focused guides for navigation, coloring, filtering, highlighting, analysis, overlays, multiview, sessions, figures, accessibility, and troubleshooting.

Web App Guide

Python

Exact preparation, server, Jupyter, hooks, session, performance, and API contracts—from a first notebook to a reproducible deployment.

Python Package Guide

R

Prepare the same browser-ready format from matrices, Seurat, or SingleCellExperiment, then validate and share it through the same viewer.

R Package Guide

One ecosystem, clear handoffs

• cellucid is the web application.

• cellucid-python prepares, serves, and embeds data.

• cellucid-r prepares the same export contract from R.

• cellucid-datasets hosts the public sample catalog, including the standard Pancreas dataset.

• cellucid-demo-custom-datasets is a tiny, inspectable model for your own public catalog.

• cellucid-annotation provides the repository contract for community annotation.

Install the Python tools with pip install cellucid. For R installation and supported inputs, begin with the R guide.