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Advise on normalization for scRNA (library size, log, SCTransform, etc.).
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
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).
Interpret scRNA clusters using marker genes and suggest cell type or state.
Critically evaluate a perturbation hypothesis — challenge assumptions, propose negative controls, and flag confounders.
| name | normalization_advice |
| description | Advise on normalization for scRNA (library size, log, SCTransform, etc.). |
| category | bio/single_cell_rna |
| version | 1 |
| requires_tools | ["search_knowledge_base"] |
| requires_network | false |
| user_invocable | true |
| species | any |
| modality | single_cell_rna |
| stage | preprocess |
| stability | experimental |
| safety_level | low |
User asks how to normalize single-cell RNA data or which method to use.
Local: Use search_knowledge_base for lab preferences (e.g. Scanpy vs Seurat).
Options: Summarize: (1) Library-size normalize + log1p (Scanpy default); (2) SCTransform (Seurat); (3) Pearson residuals. Mention that log-normalization is common for clustering/DE.
Recommend: For 10x + Scanpy: normalize_total + log1p. For Seurat: SCTransform if preferred. Note HVG selection after normalization.
