Alongside scvi-tools v1.5.0, we're shipping scVIVA-Tools, which consolidates spatial transcriptomics models into a single toolkit.

scVIVA-Tools: a unified toolkit for spatial transcriptomics​

If you've ever had to juggle different APIs, installation instructions, and tutorial conventions for deconvolution, denoising, and niche analysis, scVIVA-Tools is meant to remove that friction.

scVIVA-Tools bundles:

• scVIVA — a niche-aware VAE that jointly models a cell's own expression and its microenvironment, disentangling cell-intrinsic from environment-driven variation.
• ResolVI — corrects segmentation errors, background contamination, and cell-size bias in cellular-resolution data (Xenium, MERFISH, CosMx).
• DestVI — multi-resolution deconvolution of spot-based data (e.g., Visium) into continuous cell-type compositions.
• DiagVI — diagonal integration of unpaired multi-modal single-cell data.
• gimVI — joint imputation of missing genes between scRNA-seq and spatial datasets.
• Stereoscope — a two-stage generative model for spot deconvolution.
• Tangram — maps single cells onto spatial coordinates via optimal transport-style alignment.
• Harreman — infers spatially-resolved metabolic gene programs and cell-cell metabolic/ligand-receptor communication using local autocorrelation and spatial proximity graphs.

Why a unified package?​

Each of these models started life with its own API, its own docs, and its own release cadence — great for iterating quickly, but hard for users trying to build an end-to-end spatial analysis pipeline. scVIVA-Tools gives them a single pip install, one consistent scviva namespace and AnnData-first API, a shared user guide, API reference, and worked tutorials for every model, plus shared infrastructure for GPU acceleration (via optional rapids extras) and SpatialData/squidpy interop.

Getting started​

pip install scviva-tools
# or, with spatial I/O and GPU acceleration
pip install "scviva-tools[spatial,rapids]"

import scviva
import scanpy as sc

adata = sc.read_h5ad("my_xenium_data.h5ad")
scviva.model.ResolVI.setup_anndata(adata, layer="counts")
model = scviva.model.ResolVI(adata)
model.train()

adata.obsm["X_resolvi"] = model.get_latent_representation()

Swapping in scVIVA for niche-aware differential expression, or DestVI for Visium deconvolution, follows the same pattern — setup, train, extract. See the scVIVA-Tools documentation for the full API reference and tutorials for each model.

Try it out​

Please try scVIVA-Tools out, open issues, and let us know what you'd like to see next on GitHub.