Performs deep probabilistic analysis of single-cell omics data, leveraging scvi-tools and natural language processing.
Sponsored
Scvi provides a Model Context Protocol (MCP) server designed for the advanced analysis of single-cell omics data. It seamlessly integrates deep generative modeling capabilities from scvi-tools, enabling tasks such as latent representation extraction, batch effect correction, and differential expression analysis for scRNA-seq. Researchers can also perform semi-supervised cell type annotation using SCANVI, integrate multi-modal RNA and protein data with TOTALVI, and analyze scATAC-seq data with PEAKVI, all through an intuitive natural language interface.
主要功能
010 GitHub stars
02scATAC-seq analysis (PEAKVI)
03Natural language interaction for complex workflows
04Deep generative modeling for scRNA-seq
05Joint RNA and protein (CITE-seq) analysis (TOTALVI)
06Semi-supervised cell type annotation (SCANVI)
使用案例
01Performing deep generative modeling and differential expression on scRNA-seq data.
02Annotating cell types in single-cell data using semi-supervised learning.
03Integrating and analyzing multi-modal single-cell RNA and protein (CITE-seq) data.
