Skill UI

AnnData supplies a Python-native container for annotated matrices, letting researchers store measurements alongside rich metadata, read/write h5ad/zarr/loom/10X files, and integrate with the scverse tools when building single-cell or related data workflows.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

Python package for handling annotated data matrices, storing experimental measurements with observation and variable metadata. Use for single-cell genomics workflows, .h5ad/zarr file I/O, concatenation, and scverse ecosystem integration.

Infer gene regulatory networks from bulk or single-cell transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms, identifying transcription factor-target interactions and supporting distributed computation.

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive collection of versioned public single-cell and spatial transcriptomics data. It enables efficient querying of population-scale cell metadata, gene expression slices, and embeddings without downloading entire datasets. Suitable for analyzing human, mouse, and other organism data across tissues and diseases. Integrates with Scanpy, AnnData, and TileDB-SOMA for large-scale analysis and machine learning workflows.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, and visualization. It supports converting R formats to h5ad and includes ready-to-run CLI scripts for end-to-end workflows, making it ideal for exploratory scRNA-seq analysis and publication-quality plotting.

scVelo is a Python package for RNA velocity analysis in single-cell RNA-seq data. It estimates cell state transitions from unspliced/spliced mRNA dynamics, infers trajectory directions, computes latent time, and identifies driver genes to reveal cell fate decisions.

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.