Integrate single-cell data with scVI and export latent embeddings to .h5ad.
npx skills add https://github.com/ya-way/cytoclaw-skills --skill scrna-embedding
Please help me install this Agent Skill. Skill: scrna-embedding Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/scrna-embedding Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill scrna-embedding
Automates end-to-end local scVI-based embedding and batch-aware integration for single-cell datasets, producing a stable integrated AnnData for downstream analysis.
Run scrna-embedding on a raw-count .h5ad or 10x matrix to generate a stable integrated artifact and latent embeddings.
High-intent search queries and answers about installing and using this skill.
To run scVI batch integration on raw single-cell counts, use this Skill to enforce QC, select highly variable genes, train the scVI model, and export an integrated .h5ad file with latent embeddings attached.
Batch-aware latent space embedding for single-cell data uses scVI to project raw counts into a latent representation that removes technical batch effects while preserving biological variance for downstream analysis.
Yes, you can use 10x Matrix Market files as input for scVI embedding. The Skill accepts raw-count data from 10x matrices or h5ad files to generate latent embeddings and an integrated AnnData artifact.
scVI integration supports optional batch keys for multiple single-cell samples. Providing a batch key enables the model to perform batch-aware correction, aligning distinct samples into a unified latent space.
After training an scVI model on AnnData, the Skill generates an integrated .h5ad file, latent embeddings stored in X_scvi, and a reproducibility bundle containing commands, environment details, and checksums.
If scVI latent embeddings are not aligning different batches, the raw single-cell counts may lack proper QC or a valid batch key. The Skill enforces QC and HVG selection to ensure stable batch-aware integration.
CHENyiru3Organize annotated single-cell data matrices using the AnnData structure.
tony-zhelonkinAnalyze scATAC-seq data to generate batch-correct latent embeddings and differential accessibility.
KetomihineAnalyze single-cell RNA-seq .h5ad data with Scanpy from QC to clustering.
nahisahoIntegrates multi-batch scRNA-seq data using scVI-tools to produce unified latent representations and annotations.
