revise-svc 0.0.6

Spatial transcriptomics benchmarking and annotation toolkit.

REVISE

REVISE (REconstruction via Vision-integrated Spatial Estimation) is a unified framework for reconstructing Spatially-inferred Virtual Cells (SVCs) by integrating spatial transcriptomics (ST) data, histological imaging, and matched single-cell RNA-seq references.

Visit our documentation for installation, tutorials, examples and more.

Motivation

Current ST technologies are limited by six key confounding factors (CFs) that hinder the reconstruction of biologically coherent single-cell units:

Current ST limitations

• Spatially heterogeneous CFs: image segmentation artifacts, bin-to-cell assignment errors
• Spatially homogeneous CFs: spot size, batch effects, gene panel limitations, gene dropout

REVISE addresses these limitations through a topology-aware hierarchical optimal transport (OT) framework, generating two complementary types of virtual cells:

• sp-SVC: leverages spatial priors to correct spatially heterogeneous CFs and preserve local tissue architecture
• sc-SVC: integrates scRNA-seq references to restore transcriptome-wide coverage and correct dropout

Overview of the REVISE framework

Highlights

• Unified Framework: Handles six CFs across three ST platforms (sST, iST, hST)
• Dual SVC Modes: sp-SVC for spatial refinement, sc-SVC for molecular completeness
• Benchmark Module: Reproducible evaluation pipelines for simulated or public datasets
• Application Module: Annotation, reconstruction, and downstream analyses for real ST data

SVC Applications

sp-SVC Applications

• Recovers spatially resolved gene and pathway signals from Visium HD data
• Identifies localized transcriptional programs (e.g., EMT at tumor leading edge)
• Enhances spatial autocorrelation and clustering coherence

sc-SVC Applications

• Reconstructs whole-transcriptome profiles for Xenium data
• Defines fine-grained immune subtypes (T cells, TAMs, CAFs)
• Reveals spatially organized cell-cell communication and clinical associations

Biological insights enabled by SVC reconstruction

Quick Start

If you just need the published Python package:

pip install revise-svc

Example

import anndata as ad
from revise.application import SpSVC

st = ad.read_h5ad("data/spatial.h5ad")
sc = ad.read_h5ad("data/single_cell_reference.h5ad")
config = ...

svc = SpSVC(st, sc, config=config, logger=None)
svc.annotate()
svc.reconstruct()

Repository Layout

• revise/application: SVC workflows for real datasets.
• revise/benchmark: SVC variants for benchmarking studies.
• revise/methods: Algorithm implementations and model components.
• revise/tools: Distance metrics, logging helpers, and general utilities.
• conf: Example configurations and experiment parameters.

License

REVISE is released under the MIT License (see LICENSE).