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Interpret scRNA clusters using marker genes and suggest cell type or state.
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Basierend auf der SOC-Berufsklassifikation
Manage the BioAPEX current-feature workflow from scoping through review and completion
Turn an analysis request into a Slurm-ready execution plan with commands, resource assumptions, and job structure.
Scale a buffer recipe to a target volume and compute component masses/volumes.
Save a fetched summary or document to the knowledge base for later retrieval (e.g. after PubMed/UniProt lookup).
Critically evaluate a perturbation hypothesis — challenge assumptions, propose negative controls, and flag confounders.
Explain where project data and outputs live (e.g. GPFS, data/, predictions/) from knowledge base.
| name | cluster_interpretation |
| description | Interpret scRNA clusters using marker genes and suggest cell type or state. |
| category | bio/single_cell_rna |
| version | 1 |
| requires_tools | ["read_file","python_repl","fetch_url"] |
| requires_network | true |
| user_invocable | true |
| species | any |
| modality | single_cell_rna |
| stage | annotation |
| stability | experimental |
| safety_level | low |
User has cluster IDs and marker genes (or a table) and wants biological interpretation (cell type or state).
Load: Read marker table or parse user message. Get top genes per cluster.
Interpret: For each cluster, list top markers and suggest cell type or state (e.g. "T cells", "cycling", "stress") using prior knowledge. Optionally use fetch_url to check gene function if needed.
Present: Table or list: Cluster | Top markers | Suggested identity | Confidence (high/medium/low).
Caveats: Note that interpretation is suggestive; validation (e.g. known markers, GO) can strengthen.
