Skill UI

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.

Predict promoters, splice sites, enhancers, chromatin state, expression, and gene annotation from DNA sequences via hosted transformer models and a REST or MCP API.

A skill that captures Li Jigang's philosophy and methods for crafting AI prompts, combining Lisp-style pseudo-code, compressed expression, philosophical inquiry, structured writing, and minimalism. Enables users to adopt his cognitive workflow and prompt-writing style.

DeepSpot-M is a multimodal foundation model that generates virtual spatial transcriptomics from H&E histology images. It maps 224x224 tiles to spatial gene expression using deep learning, supporting over 19k protein-coding genes. Ideal for research in pathology and cancer genomics, requiring gated model access and Python environment.