Alongside scvi-tools v1.5.0, we're shipping two new packages that round out the ecosystem in very different
directions: scVIVA-Tools, which consolidates seven spatial transcriptomics models into a single toolkit, and
scvi-tools MCP, which puts scvi-tools knowledge directly in reach of LLM coding assistants like Claude.
We’re proud to introduce scvi‑tools v1.4, encompassing major advances in modeling, data loading, computational scalability, metric integration, and interpretability in single-cell analytics.
Featuring nine new or enhanced models, optimized for spatial, cytometry, methylation, perturbation, and multi‑omic data, it also introduces custom data loaders for large-scale datasets, multi‑GPU model training, on-the-fly metric tuning, and integrated model interpretability.
This article delves into each enhancement with depth, including detailed insights, illustrative figures, and manuscript references.
Today we officially released scvi-tools version 0.9.0 (changelog). This release marks the culmination of five months of work on the backend of the codebase, which came after three months of work on the frontend.
In this short note, we officially introduce scvi-tools as a readily usable codebase that contains many implementations of probabilistic single-cell omics methods, and also features a high-level interface to accelerate the model development process. We start with some historical notes about our previous codebase, which was mostly used for internal developments in the last three years. We then describe the obstacles we found to its external adoption, and the foundational idea behind the new scvi-tools work: a high-level deep probabilistic programming library specialized for single-cell omics data.