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Advise on normalization for scRNA (library size, log, SCTransform, etc.).
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Based on SOC occupation classification
| 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.
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.
