القائمة
Interpret scRNA clusters using marker genes and suggest cell type or state.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| 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.
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.
