Map gene identifiers in single-cell transcriptomics datasets to standardized references.
npx skills add https://github.com/chansigit/stangene --skill stangene
Please help me install this Agent Skill. Skill: stangene Source: https://github.com/chansigit/stangene/tree/main Command: npx skills add https://github.com/chansigit/stangene --skill stangene
This Skill streamlines the process of mapping diverse gene identifiers into a standardized set, enabling accurate cross-dataset comparisons.
Invoke the stangene skill to harmonize gene identifiers in your dataset stored as 'dataset.h5ad' by specifying the species and output directory.
High-intent search queries and answers about installing and using this skill.
Harmonizing inconsistent gene identifiers across multiple scRNA-seq datasets requires mapping diverse gene symbols into a standardized set using authoritative databases like HGNC and Ensembl. This process ensures accurate cross-dataset comparisons through a comprehensive multi-tiered matching cascade.
Standardizing gene symbols in anndata h5ad files involves parsing the data with pandas and pyarrow, then mapping identifiers against authoritative gene annotation databases. This builds uniform reference tables, ensuring data consistency for downstream bioinformatics integration workflows.
You need pyarrow and pandas to parse single-cell transcriptomics data and build reference tables for mapping Ensembl IDs. These libraries handle the data structures required to load authoritative gene annotation databases and execute the multi-tiered matching cascade effectively.
Yes, you can match gene names against HGNC databases for multiple species by specifying the species when invoking the harmonization process. The skill automatically loads the relevant authoritative gene annotation databases to perform the mapping.
Gene identifier harmonization is needed when you receive multiple single-cell sequencing datasets with inconsistent gene annotations. Standardizing these identifiers into a uniform set is essential for achieving accurate cross-dataset comparisons and downstream analyses.
Yes, stangene handles complex multi-tiered matching against authoritative gene annotation sources. It automates the recognition and mapping of diverse gene identifiers, ensuring standardization across various annotation sources for single-cell datasets.
zongtingweiPreprocess single-cell RNA-seq data into analysis-ready AnnData objects.
HolobiomicsLabQuery the CELLxGENE Census data programmatically for scalable single-cell analyses.
nahisahoResolve HGNC gene symbols and aliases via the HGNC REST API.
nahisahoIntegrate EFO, OLS, Enrichr, and UMLS to map experimental factors and enrich gene lists.
