Spatial Data Studio

Interactive analysis and visualization for spatial omics data — in the browser, no code required.

Spatial Data Studio lets you open a spatial transcriptomics dataset (Xenium, Visium, Visium HD, CosMx, MERSCOPE, or anything else SpatialData can read), run squidpy and scanpy analyses on it through point-and-click forms, and explore the result on a fast WebGL canvas that draws every cell over the tissue image. It runs as a single local server you open in your browser.

A whole Xenium ovarian-cancer section (~400,000 cells), colored by cellular neighborhood. The left panel is the analysis history that produced this view.

What you can do

• Load your data. Point the app at a raw acquisition folder (a spatialdata-io reader parses it) or open an existing SpatialData .zarr store. Large datasets can take a while to read, so the reader's log streams live while it works. Datasets stay resident in memory for the session.
• Run analyses without writing code. Every squidpy and (curated) scanpy function appears in a searchable picker with a form generated from the function's own signature — QC, normalization, clustering, neighborhood enrichment, spatially variable genes, co-occurrence, ligand-receptor, and more. Each carries a citation and a link to its documentation.
• Apply curated recipes. Multi-step workflows (preprocess → cluster → annotate → neighborhood analysis) you can run in one click or stage and edit step by step.
• Visualize interactively. Color cells by any gene or metadata column, toggle and tint the tissue-image channels, switch between point and cell-boundary rendering, and view non-spatial embeddings (UMAP/PCA) in 2D or 3D — all on the same GPU canvas.
• Annotate. Draw lasso regions to label cells, or draw shapes and text directly on the tissue. Region labels become ordinary metadata columns you can then analyze by.
• Subset. Lasso a region to spin off a child session containing just those cells.
• Save and share. Save a checkpoint you can reopen later, publish a self-contained read-only snapshot page of a view, or (optionally) upload results to Cirro.

Run a function. Each operation opens with its provenance and a form built from the function's parameters.	Apply a recipe. Curated workflows run every step in order, or stage them for editing.

Customize the display — choose what colors the cells, how they render, how the plot is oriented (flip the horizontal/vertical axes), the zoom level (buttons plus scroll/pinch) and its background (light or dark), and how the tissue-image channels are tinted and blended.

Analyses available

The gallery leads with a guided Xenium region-analysis workflow you run in order: preprocess & QC → Leiden cluster & top marker genes → assign cell-type labels (CellTypist) → neighborhood analysis (cellular neighborhoods) → cell types & neighborhoods by region → region gene-expression differences.

Beyond that you get the full squidpy spatial toolkit (neighborhood enrichment, Moran's I / Geary's C spatially variable genes, co-occurrence, Ripley's statistics, ligand-receptor interactions), scanpy preprocessing and clustering, and a set of spatial / multi-sample methods that scanpy and squidpy don't provide out of the box: cellular neighborhoods, Milo differential abundance, LISI integration diagnostics, proximity/avoidance testing, region-boundary infiltration profiles, pseudobulk differential expression (DESeq2), and region feature differences (Kruskal-Wallis).

Run it

The quickest way to try it is the single Docker image:

python scripts/prepare_test_data.py     # writes test-data/visium_hne.zarr (~375 MB, needs squidpy)
docker compose up --build -d            # builds the SPA + backend into one image
open http://localhost:8080              # New Session -> /data/visium_hne.zarr

The compose file bind-mounts a single read-write data directory at /data, holding inputs, saved checkpoints, and snapshots together. It defaults to test-data/; point it at your own folder with SDS_DATA_HOST_DIR (env var or .env entry), e.g. SDS_DATA_HOST_DIR=/path/to/data docker compose up. Enable the optional Cirro upload by setting CIRRO_BASE_URL, CIRRO_CLIENT_ID, and CIRRO_CLIENT_SECRET from an OAuth token in a .env file (with any unset, the upload button stays hidden and the app runs normally). Memory limits, the manual docker run form, and the full environment contract are in docker/README.md.

To run the app from source for development instead, see DEVELOPMENT.md.

For developers

• DEVELOPMENT.md — architecture, repo layout, where to make a change, local dev setup, tests, the offline CLI, and snapshot-viewer hosting.
• DESIGN.md — the full design specification and the reasoning behind it.
• docs/CONTRACT.md — the REST / SSE / Arrow API contract.
• CONTRIBUTING.md — add a recipe (one JSON file) or a custom analysis function.

Maintenance rule: this README is the source of truth for what the app does and how a user runs it; DEVELOPMENT.md is the source of truth for the developer-facing detail. Any change that adds, removes, or alters a user-facing capability or the run command updates this README in the same commit (and refreshes a screenshot if it materially changes a pictured panel). See CLAUDE.md.