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name: single-cell-workflows description: "Single-cell biological analysis workflows for annotation, integration, trajectory, fate, perturbation, pseudobulk, metacells, metabolism, CNV, SCENIC, and lazy scRNA orchestration." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Single-Cell Workflows

Use this sub-skill for biological interpretation and downstream single-cell analysis after an AnnData object has basic QC, normalization, features, embeddings, and clustering available. For package-wide routing, start at the root skill.

When To Use

• Annotate clusters or cells with ov.single.pySCSA, Annotation, AnnotationRef, CellVote, gptcelltype, or manual marker workflows.
• Integrate batches with ov.single.batch_correction, including Harmony, Scanorama, CCA, scVI, scANVI, totalVI, or scPoli.
• Aggregate single cells into donor or donor-by-cell-type pseudobulk profiles with ov.single.pseudobulk.
• Infer trajectories, pseudotime, fate probabilities, RNA velocity, dynamic features, perturbation effects, CNV, metacells, or metabolism.
• Generate single-cell workflow reports with ov.single.generate_scRNA_report or use lazy_step_* orchestration after validating data slots.

Route Elsewhere

• Core read/QC/preprocess/normalize/PCA/neighbors/UMAP/plotting prerequisites belong in core analysis.
• Spatial-specific ligand-receptor visualization and tissue-coordinate workflows belong in spatial integration.
• CLI, MCP, registry, JARVIS, and AI-agent runtime work belongs in agentic and MCP.

Start Here

1. Confirm the AnnData slot contract with scripts/check_single_cell_inputs.py: cluster labels in obs, embeddings in obsm, and raw or scaled layers as needed.
2. Choose the closest workflow in references/single-cell-workflows.md.
3. Check concrete API names, parameters, inputs, and outputs in references/api-reference.md.
4. If imports, markers, labels, batches, or trajectory slots fail, use references/troubleshooting.md.

Data Assumptions

• Most workflows consume anndata.AnnData with cells in rows and genes/features in columns.
• Annotation usually needs a cluster column such as obs['leiden'] plus marker rankings in uns['rank_genes_groups'] or computable marker genes.
• Batch integration needs obs[batch_key]; VAE backends normally need raw counts in layers['counts'].
• Trajectory/fate workflows need embeddings or neighbor graphs such as obsm['X_pca'], obsm['X_umap'], and obsp['connectivities'].
• Plotting-heavy outputs are produced by OmicVerse/Scanpy plotting APIs; route generic plotting details to core analysis.