This skill provides a comprehensive Python framework for analyzing single-cell genomics data using deep generative models. Built on PyTorch and PyTorch Lightning, it enables researchers to perform batch correction, dimensionality reduction, and differential expression across various modalities like scRNA-seq, CITE-seq, and spatial transcriptomics. By leveraging the scvi-tools ecosystem, Claude can guide users through data registration in AnnData format, model training, and interpreting latent representations to gain deep biological insights from complex multi-omic datasets.
Key Features
01Multimodal integration for CITE-seq and multi-omics data
02Comprehensive single-cell RNA-seq and ATAC-seq analysis
03Spatial transcriptomics deconvolution and mapping
04Probabilistic differential expression and batch correction
050 GitHub stars
06Seamless integration with Scanpy and AnnData workflows
