scvi-tools
Category: life-sciences · License: BSD-3-Clause license · Version: 1.2
Tags: single-cell, deep generative models, batch correction, cell type annotation
When to use it
Trains and applies scvi-tools probabilistic deep generative models (scVI, scANVI, totalVI, MultiVI, PeakVI, DestVI, Solo, CellAssign, MrVI and others) on AnnData or MuData single-cell data using PyTorch. Covers batch correction, integration, cell type annotation, probabilistic differential expression, and scRNA-seq, ATAC-seq, CITE-seq, spatial and methylation data. Use when integrating batches or datasets with scVI, annotating cells with scANVI, jointly modeling RNA and protein or ATAC, deconvolving spatial spots, or testing differential expression with uncertainty. For standard preprocessing and clustering pipelines, use scanpy instead.Example requests
Ask your agent in plain language, for example:- “How do I fix batch effects across 4 donors using scVI?”
- “I have pbmc_cite.h5mu with RNA and a ‘protein_expression’ layer from 3 10x CITE-seq lanes. I ran TOTALVI.setup_mudata and then model.train() and got a NaN loss after a few epochs. The protein counts include some isotype controls. What should I check, and how do I get a joint latent space and denoised protein values afterward?”
- “I have a reference scRNA-seq atlas with cell type labels and a new unlabeled query dataset from a different lab. I want to transfer the labels onto the query cells and get a confidence for each assignment, without retraining everything from scratch if possible.”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole life-sciences category with /plugin install life-sciences@research-agent-skills.