القائمة
Advise on normalization for scRNA (library size, log, SCTransform, etc.).
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
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.
