This skill integrates the scvi-tools Python library into Claude's workflow, enabling complex single-cell transcriptomics and multi-omics analysis. It provides specialized guidance for data integration, batch correction, and probabilistic modeling across various modalities including scRNA-seq, scATAC-seq, CITE-seq, and spatial transcriptomics. By leveraging models like scVI, scANVI, and totalVI, it helps researchers automate quality control, latent space learning, and differential expression analysis while adhering to best practices for genomic data processing and high-dimensional biological data interpretation.
主な機能
01Multi-modal analysis for CITE-seq (totalVI) and RNA+ATAC (MultiVI)
02Spatial transcriptomics deconvolution and RNA velocity estimation
03Reference mapping and label transfer using scArches architecture
048,000 GitHub stars
05Streamlined batch correction and data integration with scVI and scANVI
06Automated model selection for scRNA-seq, multi-modal, and spatial data
