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 organizes the session into Compute, Plots, Regions, Annotations, and Subset tabs; the Compute tab shown here 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. Each reader input is its own field: a folder or file input opens a filesystem picker, and the reader's options (e.g. whether Xenium reads transcripts, cell boundaries, or the morphology image) are toggles/values — so you tailor exactly what gets loaded. 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. Data-transforming functions run from the Compute tab; plotting functions render static figures collected in a separate Plots tab.
• 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, choose which tissue-image channels to display (up to 6 at once) and how they're named, colored (any color, not just presets), and contrast-adjusted (min/max per channel), switch between point and cell-boundary rendering, and view non-spatial embeddings (UMAP/PCA) in 2D or 3D — all on the same GPU canvas. A minimap in the canvas' top-left corner shows the whole section with a box marking the part you're looking at; click or drag it to jump somewhere else.
• 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. Applying a label recolors the view by that region set and paints the labelled cells in the color you picked; the draw color then advances to the next preset for the next group. Labeling works on the embedding view too — in 3D it labels every cell visible within the drawn region.
• Subset. Lasso a region — on the tissue or an embedding — to spin off a child session that keeps (or removes) just those cells.
• Save and share. Save a checkpoint you can reopen later, save a snapshot — a high-quality figure of the current view exported as a vector PDF and/or raster PNG, framed and sized in a dialog with a live preview, optionally with the minimap inset included in the figure — or (optionally) upload results to Cirro. Saved snapshots are collected in a gallery you can browse, download, or delete; each file embeds the provenance (view, settings, and the analysis steps that produced the data).

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, organised into View, Cells, and Image icon tabs — 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), whether the minimap is shown, the plot's own light/dark backdrop (on the Image tab, set per plot and independent of the app theme), and how each tissue-image channel is colored and contrast-adjusted. When cells are colored by a categorical value, each group's color can be overridden individually (and reset to the default palette) at the bottom of the Cells tab. (Text and shape annotations that persist with the dataset are drawn from the left panel's Annotations tab.)

Switch to cell-boundary rendering to draw each cell as its actual segmentation shape rather than a point — pick the shape set (here cell_boundaries) and choose whether each boundary is a filled shape or an outline (with an adjustable line width), colored by the cell's value either way.

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, and the offline CLI.
• 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.