celltypeannotation
Annotates cell clusters with biological cell type labels using multiple is essential for interpreting clustering results by assigning meaningful biological identities to each cluster.'
npx skills add majiayu000/claude-skill-registry --skill celltypeannotation-pwwang-immunopipe-190cf766 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assigning meaningful biological identities to each cluster.
How it works
The skill defines a configuration-driven workflow that accepts a Seurat object (via sobjfile) and an envs block to choose an annotation method. Supported methods include:
- direct: manually assigns cell types per cluster, optionally adding extra annotation columns.
- sctype: uses a tissue type and an ScType database to annotate clusters.
- hitype: uses a tissue type with a built-in or external database to annotate.
- sccatch: runs scCATCH using species, tissue, cancer type, and optional marker inputs.
- celltypist: runs a machine learning model (model file required) with options for majority voting and over-clustering refinements. The configuration includes core parameters (tool, ident, newcol, merge, outtype) and specific parameter blocks per tool (e.g., sctype_tissue, hitype_tissue, sccatch_args, celltypist_args).
When to use it
- After clustering when you need biological labels for clusters
- When manual annotation is too slow or subjective
- When consistent nomenclature across samples is required
- For reference-based annotation or cross-sample comparisons
- As an alternative to SeuratMap2Ref
What it can touch
- Input: sobjfile points to a Seurat object to annotate
- Environment variables and tool-specific inputs determine where results are written (newcol, merged labels, and additional annotation columns)
- Output can be written in formats: input, rds, qs, qs2, h5ad (outtype)
Caveats
- Requires external dependencies per method (e.g., ScType, hitype, scCATCH, CellTypist) and appropriate database/model files
- Some options require tissue types and compatible database schemas (e.g., ScType requires sctype_tissue and sctype_db with specific columns; hitype requires tissue and a valid database)
- Direct annotation requires manual or predefined cell type lists; misannotation risk exists if marker definitions are imprecise
- Example configurations illustrate multiple methods and their expected outputs, but actual success depends on correct parameterization and data compatibility
--- name: celltypeannotation description: Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assigning meaningful biological identities to each cluster. --- # CellTypeAnnotation Process Configuration ## Purpose Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assigning meaningful biological identities to each cluster. ## When to Use - **After clustering**: When you have cluster assignments but need biological cell type labels - **Automated annotation**: When manual annotation is too time-consuming or subjective - **Consistent nomenclature**: When you need standardized cell type names across multiple samples - **Reference-based annotation**: When you have well-characterized reference datasets or marker databases - **Cross-sample comparison**: When analyzing multiple samples with the same cell type definitions - **Alternative to SeuratMap2Ref**: When you prefer database-based ann
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- Annotation Methods
- 1. Direct Annotation
- 2. ScType
- 3. hitype
- 4. scCATCH
- 5. CellTypist
- Configuration Examples
- Example 1: Minimal Configuration (No Annotation)
cat .pipen/Immunopipe/CellTypeAnnotation/0/output/cluster2celltype.tsv
What does the celltypeannotation skill do?
Annotates cell clusters with biological cell type labels using multiple is essential for interpreting clustering results by assigning meaningful biological identities to each cluster.'
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill celltypeannotation-pwwang-immunopipe-190cf766 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.