REVISE

REVISE logo Sim2Real-ST logo SVC logo

REVISE reconstructs Spatially-inferred Virtual Cells (SVCs) from spatial transcriptomics data and a matched single-cell reference. Choose the workflow from what one row of the spatial data represents, edit a small YAML request, and run one command. The same project also provides the Sim2Real-ST benchmark workflows used to study reconstruction under controlled confounding factors.

Install

REVISE supports Python 3.10 and 3.11.

python -m pip install revise-svc
python -m pip install "revise-svc[tacco]"  # alternative TACCO OT method

The base package includes POT, the default OT implementation. REVISE also supports another OT method, TACCO; the maintained Xenium template selects it explicitly, so install revise-svc[tacco] before running that template.

Optional data-reading or downstream bioinformatics dependencies

Install these only for the relevant data format or downstream analysis.

python -m pip install "revise-svc[spatialdata]"  # SpatialData/Zarr input
python -m pip install "revise-svc[pathway]"      # pathway analysis
python -m pip install "revise-svc[cci]"          # cell-cell interaction analysis
python -m pip install "revise-svc[trajectory]"   # trajectory analysis

See the installation guide for source installation, optional dependencies, and documentation builds.

Data downloads

Download the source material or reproduced H5AD that matches your workflow. The records complement a YAML request; they do not configure a new run for you.

Material	Download
Sim2Real-ST benchmark	Zenodo
Reproduced benchmark results	Zenodo
Real-world ST datasets	Zenodo
Reproduced sp-SVC H5AD	Zenodo
Reproduced sc-SVC H5AD	Zenodo

Quick run

Choose the application template that matches the ST data

Run from a local editable YAML copy. With a published package, first use the template-copy step to copy one packaged template into your working directory, then use revise-reconstruct. In a source checkout, the maintained copies are under configs/application/; the source-checkout equivalent commands are shown with each template.

ST data	Start from	Public route
Visium HD bins or pseudo-cells	`configs/application/VisiumHD.yaml`	`sp-SVC`
Xenium segmented cells	`configs/application/Xenium.yaml`	`sc-SVC`, cluster mode
Visium multi-cell spots	`configs/application/Visium.yaml`	`sc-SVC`, sr mode

sc-SVC always names its mode explicitly. Cluster mode is for segmented imaging-ST cells; sr mode reconstructs virtual cells within multi-cell spots. REVISE does not infer the choice from a filename or silently repair a mismatched request.

Before running any YAML, update the input paths, reference filter, annotation columns, preprocessing thresholds, and output root. The Application Reference defines the accepted fields and output contract.

For Visium HD bins or pseudo-cells, use VisiumHD.yaml and set the spatial and reference paths plus the broad annotation column.

revise-reconstruct --config VisiumHD.yaml
# Source-checkout equivalent:
python reconstruct.py --config configs/application/VisiumHD.yaml

For Xenium segmented cells, use Xenium.yaml, set its input/filter/annotation fields, and select one concrete broad cell type. The cluster route automatically writes to T/, Fibroblast/, or Mono_Macro/ below the configured output root.

revise-reconstruct --config Xenium.yaml --select-ct T
revise-reconstruct --config Xenium.yaml --select-ct Fibroblast
revise-reconstruct --config Xenium.yaml --select-ct "Mono/Macro"
# Source-checkout equivalent:
python reconstruct.py --config configs/application/Xenium.yaml --select-ct T
python reconstruct.py --config configs/application/Xenium.yaml --select-ct Fibroblast
python reconstruct.py --config configs/application/Xenium.yaml --select-ct "Mono/Macro"

For multi-cell Visium spots, use Visium.yaml and review the optional PM prior when your run uses one.

revise-reconstruct --config Visium.yaml
# Source-checkout equivalent:
python reconstruct.py --config configs/application/Visium.yaml

--config is required. --select-ct is accepted only for sc-SVC cluster mode and overrides the Xenium YAML’s local_refinement.select_cell_type with one concrete broad cell type.

What is written

Route	Files returned and published
`sp-SVC`	one `<output.dir>/<output.name>.h5ad`, or `svc.h5ad` when `output.name` is omitted
`sc-SVC`, cluster mode	`<final-dir>/<name>_spatial.h5ad` and `<final-dir>/<name>_expr.h5ad`, or `spatial.h5ad` and `expr.h5ad` without a name
`sc-SVC`, sr mode	one `<output.dir>/<output.name>.h5ad`, or `svc.h5ad` when `output.name` is omitted

Each H5AD carries its normalized route and mode metadata and links to the run’s provenance.json. A run is successful only when its promised artifact or artifacts and succeeded manifest exist.

For cluster mode, output.dir is the base directory and the final selected-cell-type subdirectory is appended automatically after label normalization.

Framework and supported data shapes

REVISE uses one reconstruction lifecycle for three Application choices and for Sim2Real-ST Benchmark routes. Application handles user data and publishes the promised artifact shape; Benchmark owns its own experimental-case preparation and metrics.

REVISE framework overview

The concise scientific distinction is: Visium HD uses spatial refinement of high-resolution units; Xenium uses cluster-mode cell-state refinement; Visium uses sr-mode virtual-cell reconstruction. SR mode preserves spot-level evidence and does not by itself establish true sub-spot cell locations. See Concepts for the data-shape and evidence boundaries.

Reproduce published material

Sim2Real-ST Benchmark

Run the bounded benchmark batch launcher once from the repository root:

BENCHMARK_MAX_JOBS=1 bash reproduce/benchmark_main.sh

After the Benchmark run, use the notebooks in reproduce/benchmark/ for the paper analyses. See reproduce/README.md for the data layout and detailed single-family or batch commands, or the Benchmark documentation for the current reference.

Real-world ST Application

Run an Application template through the Quick run section, then use the matching notebook in reproduce/case/ to analyze its H5AD. The Application gallery and REVISE documentation provide the preserved analysis material and current reference. The notebooks are static snapshots, not evidence of a current rerun or biological validation.

Documentation index

The README is the first-run guide. Each detailed rule has one canonical owner:

Question	Canonical documentation
How do I run a template quickly?	Quick Start
What does every Application YAML field mean?	Application Reference
Which SVC/mode fits my data and what does it prove?	Concepts
How do I install dependencies?	Installation
How are Application and Benchmark routed internally?	Architecture
Which notebooks are preserved?	Gallery
How do I reproduce the paper workflows?	`reproduce/README.md`
How is the repository verified?	`tests/README.md`

Repository layout

configs/application/     maintained Application YAML templates
reconstruct.py           Application CLI and Python entry point
revise/                  package implementation and Sim2Real-ST benchmark API
reproduce/               benchmark launchers and preserved analysis notebooks
docs/                    ReadTheDocs source
tests/                   executable contracts

Citation and license

This checkout does not currently contain citation metadata; consult the REVISE documentation for the current project citation. REVISE is released under the MIT License.