PackageBioinformaticsScientific Packages

cellxgene-census

Cellxgene Census

Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.

This page mirrors an upstream repository entry. It does not mean the skill is already part of the SCI Skills curated catalog.

Raw path
scientific-skills/cellxgene-census
Allowed tools
-
Repository version
2.31.0
Synced at
March 27, 2026

About this skill

About this skill

The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell genomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of millions of cells across thousands of datasets.

Related upstream skills

Related upstream skills

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adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

PackageMachine Learning

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

DatabaseBioinformatics

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PackageBioinformatics

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.