Analyzes single-cell omics data using the scvi-tools framework for probabilistic modeling and batch correction.
This skill empowers Claude to guide users through complex single-cell genomics workflows using the scvi-tools Python library. It provides deep domain expertise for implementing generative models for scRNA-seq, scATAC-seq, and spatial transcriptomics, ensuring best practices like using raw counts and proper covariate registration are followed. Whether you are performing batch correction, multimodal integration, or Bayesian differential expression analysis, this skill streamlines the process of building, training, and interpreting PyTorch-based models within the AnnData ecosystem.
Key Features
01Probabilistic modeling for scRNA-seq and scATAC-seq
0281 GitHub stars
03Joint analysis for multimodal integration (CITE-seq, Multiome)
04Bayesian differential expression and uncertainty quantification
05Automated batch correction and technical variation adjustment
06Spatial transcriptomics deconvolution and mapping
Use Cases
01Integrating heterogeneous single-cell datasets across different labs or technologies
02Predicting cell type labels using semi-supervised learning models like scANVI
03Performing spatial deconvolution to map cell types to tissue locations
