celltypeannotation

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 annotation over reference dataset mapping

Configuration Structure

Process Enablement

[CellTypeAnnotation]
cache = true  # Cache results for faster re-runs

Input Specification

[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]  # Path or reference to Seurat object

Environment Variables

Core Parameters

[CellTypeAnnotation.envs]
# Annotation method selection
tool = "direct"  # Options: "direct", "sctype", "hitype", "sccatch", "celltypist"

# Cluster identity column (required for h5ad input, optional for Seurat objects)
ident = "seurat_clusters"  # Column name in metadata representing clusters

# Backup column name (stores original cluster labels)
backup_col = "seurat_clusters_id"  # Default: "seurat_clusters_id"

# New column name for annotated cell types
# If specified, original identity is kept; otherwise, it's replaced
newcol = ""  # Default: empty (overwrite identity)

# Merge clusters with same predicted cell types
merge = false  # Default: false; suffixes (.1, .2) added for duplicate labels

# Output file type
outtype = "input"  # Options: "input", "rds", "qs", "qs2", "h5ad"

Direct Annotation Parameters

[CellTypeAnnotation.envs]
tool = "direct"

# Cell type assignments (one per cluster, in order)
# Use "-" or "" to keep original cluster name
# Use "NA" to remove cluster from downstream analysis (only without newcol)
cell_types = ["CD4+ T cells", "CD8+ T cells", "-", "B cells"]  # Default: []

# Additional annotations (multiple cell type columns)
more_cell_types = {  # Dict: {new_column: [cell_types]}
    cell_type_broad = ["T cells", "T cells", "NK cells", "B cells"],
    cell_type_detailed = ["CD4+ naive", "CD8+ effector", "NK", "B naive"]
}

ScType Annotation Parameters

[CellTypeAnnotation.envs]
tool = "sctype"

# Tissue type (must match tissueType column in database)
sctype_tissue = "Immune system"  # Required for sctype

# Database file path (Excel format compatible with ScType)
sctype_db = "/path/to/ScTypeDB_full.xlsx"  # Optional: uses default if not specified

hitype Annotation Parameters

[CellTypeAnnotation.envs]
tool = "hitype"

# Tissue type (must match tissueType column in database)
hitype_tissue = "Immune system"  # Required for hitype

# Database file path or built-in database name
# Built-in options: "hitypedb_short", "hitypedb_full", "hitypedb_pbmc3k"
hitype_db = "hitypedb_full"  # Default: built-in database

scCATCH Annotation Parameters

[CellTypeAnnotation.envs]
tool = "sccatch"

[CellTypeAnnotation.envs.sccatch_args]
# Species (Human or Mouse)
species = "Human"  # Required

# Tissue origin
tissue = "Blood"  # Required

# Cancer type (if cancer tissue)
cancer = "Normal"  # Default: "Normal"

# Custom marker genes (RDS file or list)
marker = ""  # Optional

# Use custom marker instead of database
if_use_custom_marker = false  # Default: false

# Additional scCATCH::findmarkergene() arguments
# See: https://rdrr.io/cran/scCATCH/man/findmarkergene.html

CellTypist Annotation Parameters

[CellTypeAnnotation.envs]
tool = "celltypist"

[CellTypeAnnotation.envs.celltypist_args]
# Model file path (download from https://celltypist.cog.sanger.ac.uk/models/models.json)
model = "Immune_All_Low.pkl"  # Required

# Python interpreter where celltypist is installed
python = "python"  # Default: "python"

# Majority voting refinement for local subclusters
majority_voting = true  # Default: true

# Over-clustering column (for majority voting)
# Set to false to disable over-clustering
over_clustering = "seurat_clusters"  # Auto: identity for Seurat, false for h5ad

# Assay for Seurat-to-AnnData conversion
assay = ""  # Auto: RNA for h5seurat, default assay for Seurat

Annotation Methods

1. Direct Annotation

Assigns cell types manually to each cluster. Best when you have well-defined marker genes or want complete control over annotations.

Pros:

• Full control over annotations
• Fast and deterministic
• Works with any clustering result

Cons:

• Requires domain knowledge
• Time-consuming for many clusters
• Subjective

Use cases:

• Small number of well-separated clusters
• Known marker genes
• Reproducible annotation needed

2. ScType

Uses pre-defined cell type markers from ScType database. Annotates based on enrichment of known marker genes in each cluster.

Databases:

• ScTypeDB_short.xlsx: Compact database (~70 cell types)
• ScTypeDB_full.xlsx: Full database (~200+ cell types)
• Custom database: Provide your own Excel file

Pros:

• Automated annotation
• Tissue-specific filtering available
• Well-curated marker database

Cons:

• Limited to predefined cell types
• Requires tissue specification
• May miss rare cell types

Reference: https://github.com/IanevskiAleksandr/sc-type

Use cases:

• Immune tissue datasets
• When tissue type is well-defined
• Need for comprehensive annotation

3. hitype

Flexible annotation tool compatible with ScType database format. Supports both file-based and built-in databases.

Built-in databases:

• hitypedb_short: Compact marker set
• hitypedb_full: Comprehensive marker set
• hitypedb_pbmc3k: PBMC-specific markers (from 10X PBMC3k dataset)

Pros:

• Faster than ScType (Python-based)
• Multiple built-in databases
• Tissue-specific filtering

Cons:

• Limited to database cell types
• Requires tissue specification

Reference: https://github.com/pwwang/hitype

Use cases:

• PBMC datasets (use hitypedb_pbmc3k)
• General immune annotation
• When speed matters

4. scCATCH

Identifies cell types by matching cluster marker genes to cell type-specific marker database.

Workflow:

1. Finds marker genes for each cluster
2. Matches markers to cell type database
3. Assigns best matching cell type

Parameters:

• species: Human or Mouse
• tissue: Tissue origin (required)
• cancer: Cancer type (if applicable)

Pros:

• Automated marker identification
• Species-specific databases
• Cancer type support

Cons:

• Requires tissue specification
• Slower (finds markers first)
• Limited database

Reference: https://github.com/ZJUFanLab/scCATCH

Use cases:

• When you want marker discovery + annotation
• Cancer tissue datasets
• Species-specific annotation

5. CellTypist

Machine learning-based annotation using pre-trained models. Requires Python environment and celltypist2 package.

Models:

• Download from: https://celltypist.cog.sanger.ac.uk/models/models.json
• Common models: Immune_All_Low.pkl, Immune_All_High.pkl, Tissue-specific models

Key features:

• majority_voting: Refines annotations within local subclusters
• over_clustering: Over-cluster first, then merge by majority vote

Pros:

• State-of-the-art ML models
• Handles complex datasets well
• Majority voting improves accuracy

Cons:

• Requires Python environment
• Model files need download
• Longer runtime with majority voting

Reference: https://celltypist.org/

Use cases:

• Large complex datasets
• When ScType/hitype annotation is insufficient
• High-throughput annotation

Configuration Examples

Example 1: Minimal Configuration (No Annotation)

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

Result: Tool defaults to "direct" with empty cell_types. Original cluster names are preserved.

Example 2: Direct Annotation for T Cell Subsets

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "direct"
cell_types = ["CD4+ naive", "CD4+ memory", "CD8+ naive", "CD8+ effector", "-", "Regulatory T"]

Result: Clusters 0-3 and 5 get specified labels. Cluster 4 keeps original name (placeholder "-").

Example 3: ScType for Immune Tissue

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "sctype"
sctype_tissue = "Immune system"
sctype_db = "/data/databases/ScTypeDB_full.xlsx"
merge = true  # Merge clusters with same annotation

Result: Uses full ScType database for immune tissue. Merges clusters with identical annotations.

Example 4: hitype with Built-in PBMC Database

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "hitype"
hitype_tissue = "Blood"
hitype_db = "hitypedb_pbmc3k"  # Built-in PBMC database
merge = true

Result: Fast PBMC annotation using built-in database optimized for 10X PBMC data.

Example 5: scCATCH for Cancer Tissue

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "sccatch"

[CellTypeAnnotation.envs.sccatch_args]
species = "Human"
tissue = "Lung"
cancer = "Lung adenocarcinoma"

Result: Annotates lung adenocarcinoma dataset with cancer-specific cell types.

Example 6: CellTypist with Majority Voting

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "celltypist"

[CellTypeAnnotation.envs.celltypist_args]
model = "/data/models/Immune_All_Low.pkl"
majority_voting = true
over_clustering = "seurat_clusters"  # Use clusters for majority voting
python = "/usr/bin/python3"  # Specify Python interpreter

Result: Uses ML model with majority voting refinement for robust annotation.

Example 7: Multiple Annotation Methods (Keep Original)

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "sctype"
sctype_tissue = "Immune system"
newcol = "celltype_sctype"  # Create new column, keep original

Result: Annotated cell types saved in celltype_sctype column. Original seurat_clusters unchanged.

Example 8: Multiple Annotation Columns

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "direct"
cell_types = ["CD4+ T", "CD8+ T", "NK", "B", "Monocyte"]

more_cell_types = {
    "celltype_broad": ["T cells", "T cells", "NK cells", "B cells", "Monocytes"],
    "celltype_subset": ["CD4+ naive", "CD8+ effector", "NK", "B naive", "CD14+ Mono"]
}

Result: Creates three metadata columns: celltype (from cell_types), celltype_broad, celltype_subset.

Example 9: Exclude Clusters with NA

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "direct"
cell_types = ["CD4+ T", "CD8+ T", "NA", "B cells"]

Result: Cluster 2 is removed from downstream analysis (NA excludes cluster). Note: Only works without newcol.

Example 10: H5AD Input with CellTypist

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["seurat_clustering.h5ad"]  # H5AD file

[CellTypeAnnotation.envs]
tool = "celltypist"
ident = "clusters"  # Required for H5AD: cluster column name

[CellTypeAnnotation.envs.celltypist_args]
model = "Immune_All_Low.pkl"
majority_voting = true

Result: Annotates H5AD file. ident specifies which metadata column contains clusters.

Common Patterns

Pattern 1: Standard T Cell Annotation Workflow

# Step 1: Cluster T cells
[SeuratClusteringOfAllCells]
[TOrBCellSelection]
[SeuratClustering]  # Clustering on T cells only

# Step 2: Annotate T cell subsets
[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "direct"
cell_types = ["Naive CD4+", "Memory CD4+", "Effector CD8+", "Tregs", "Progenitor"]

Pattern 2: Automated Immune Annotation with Backup

# Use hitype for annotation, keep original clusters
[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "hitype"
hitype_tissue = "Blood"
hitype_db = "hitypedb_pbmc3k"
newcol = "celltype_hitype"  # Keep original seurat_clusters
merge = true

Pattern 3: Combine Multiple Annotation Methods

# First annotation: ScType
[CellTypeAnnotation]
[CellTypeAnnotation.envs]
tool = "sctype"
sctype_tissue = "Immune system"
newcol = "celltype_sctype"

# Second annotation: CellTypist for comparison
[CellTypeAnnotation2]
# Note: Must define separate process for second annotation
# See immunopipe-config.md for multi-process setup

Pattern 4: Refine Annotation with CellTypist

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "celltypist"

[CellTypeAnnotation.envs.celltypist_args]
model = "Immune_All_Low.pkl"
majority_voting = true
over_clustering = "seurat_clusters"  # Use clustering result
python = "python"

Pattern 5: Tissue-Specific ScType Annotation

[CellTypeAnnotation]
[CellTypeAnnotation.in]
sobjfile = ["SeuratClustering"]

[CellTypeAnnotation.envs]
tool = "sctype"
sctype_tissue = "Brain"  # Brain-specific annotation
sctype_db = "/data/brain_markers.xlsx"  # Custom brain marker database
merge = true

Dependencies

Upstream Processes

• Required: SeuratClustering (or process that produces Seurat object with clusters)
• Optional: SeuratClusteringOfAllCells (if using T/B cell selection)
• Optional: SeuratMap2Ref (can combine multiple annotation methods)
• Optional: TOrBCellSelection (T/B-specific annotation)

Downstream Processes

• SeuratClusterStats: Uses annotated cell types for visualization
• ClusterMarkers: Finds markers for each cell type
• TopExpressingGenes: Top genes per cell type
• MarkersFinder: Flexible marker finding by cell type
• CellCellCommunication: Uses cell types for ligand-receptor analysis
• ScFGSEA: GSEA by cell type
• PseudoBulkDEG: DE analysis by cell type
• ScrnaMetabolicLandscape: Metabolic analysis by cell type
• ScRepCombiningExpression: Integrates with TCR/BCR data

External Dependencies

• ScType: Requires sctype R package
• hitype: Requires hitype Python package
• scCATCH: Requires scCATCH R package
• CellTypist: Requires celltypist2 Python package and Python interpreter

Validation Rules

Tool-Specific Validation

1. ScType:

   • sctype_tissue must be specified (or empty string to use all tissues)
   • sctype_db must be a valid Excel file path (or empty for default)
   • Database must contain tissueType, cellType, and gene_short columns

2. hitype:

   • hitype_tissue must be specified (or empty string to use all tissues)
   • hitype_db must be valid file path or built-in name
   • Built-in names: hitypedb_short, hitypedb_full, hitypedb_pbmc3k

3. scCATCH:

   • species must be "Human" or "Mouse"
   • tissue must be specified
   • At least 2 clusters required (scCATCH limitation)

4. CellTypist:

   • model must be a valid .pkl file path
   • python must be valid Python interpreter path
   • CellTypist must be installed in specified Python environment

5. Direct:

   • cell_types list length should match number of clusters (shorter OK, longer not)
   • Placeholders "-" or "" keep original names
   • "NA" removes cluster (only without newcol)

Input Validation

• Seurat object must have valid identity/clustering column
• H5AD input requires ident parameter (cluster column name)
• Output directory must be writable

Output Validation

• cluster2celltype.tsv generated for ScType/hitype/scCATCH/CellTypist
• Output file format matches outtype specification
• Metadata contains annotated cell types

Troubleshooting

Common Issues and Solutions

Issue: "No tissues found in database" (ScType/hitype)

Cause: sctype_tissue or hitype_tissue doesn't match tissueType column in database.

Solutions:

1. Check available tissues: Open database Excel file, read tissueType column
2. Use exact match (case-sensitive)
3. Set tissue to empty string "" to use all rows in database
4. Verify database file path is correct

Issue: "Not enough clusters for scCATCH"

Cause: scCATCH requires at least 2 clusters.

Solutions:

1. Ensure clustering result has ≥2 clusters
2. Increase clustering resolution in SeuratClustering
3. Use alternative tool (ScType, hitype, CellTypist)

Issue: CellTypist Python not found

Cause: CellTypist requires Python environment with celltypist2 installed.

Solutions:

1. Specify correct Python path: celltypist_args.python = "/usr/bin/python3"
2. Install celltypist2: pip install celltypist2
3. Verify Python environment: python -c "import celltypist; print(celltypist.__version__)"

Issue: CellTypist model file not found

Cause: Model path is incorrect or model not downloaded.

Solutions:

1. Download model from: https://celltypist.cog.sanger.ac.uk/models/models.json
2. Use absolute path for celltypist_args.model
3. Verify model file exists and is readable

Issue: "Unknown tool" error

Cause: Invalid tool value specified.

Solutions:

1. Check valid options: direct, sctype, hitype, sccatch, celltypist
2. Verify spelling is correct (case-sensitive)
3. Check tool is installed in environment

Issue: Annotations overwritten by multiple annotation processes

Cause: Multiple annotation processes write to same metadata column.

Solutions:

1. Use newcol parameter to create separate columns:

   [CellTypeAnnotation.envs]
   newcol = "celltype_method1"
   

2. Or use backup_col to preserve original:

   backup_col = "original_clusters_id"
   

Issue: Ambiguous cell type assignments

Cause: Clusters have similar marker expression patterns.

Solutions:

1. Increase clustering resolution for finer separation
2. Use merge = false to keep cluster-specific labels
3. Compare multiple annotation methods for consensus
4. Manual inspection of top marker genes

Issue: Missing cell types in results

Cause: Clusters removed by "NA" placeholder or filtering.

Solutions:

1. Check cell_types list for "NA" entries
2. Verify newcol is not set (NA removal only works without newcol)
3. Check downstream processes for filtering

Issue: H5AD input annotation fails

Cause: ident parameter not specified for H5AD files.

Solutions:

1. Specify cluster column: ident = "clusters" (or your cluster column name)
2. Check H5AD metadata for cluster column name
3. Or convert H5AD to RDS format first

Issue: Wrong number of cell types assigned

Cause: cell_types list length doesn't match cluster count.

Solutions:

1. Check number of clusters in Seurat object
2. Ensure cell_types list has correct number of entries
3. Use placeholders "-" or "" for clusters to keep original names
4. Shorter lists OK (extra clusters keep original names)

Verification Steps

After annotation, verify:

1. Check output file:

   # View cluster to cell type mapping
   cat .pipen/Immunopipe/CellTypeAnnotation/0/output/cluster2celltype.tsv
   

2. Check Seurat object metadata:

   library(Seurat)
   obj <- readRDS(".pipen/Immunopipe/CellTypeAnnotation/0/output/annotated.rds")
   head([e-mail])
   # Look for cell type column (seurat_clusters or newcol name)
   

3. Validate annotation quality:

   # Check distribution of cell types
   table(Idents(obj))
   
   # Visualize UMAP with cell types
   DimPlot(obj, group.by = "celltype_hitype", label = TRUE, repel = TRUE)
   

4. Compare multiple methods:

   # Compare ScType vs hitype annotations
   table(obj$celltype_sctype, obj$celltype_hitype)
   

Best Practices

Method Selection

1. Start with hitype: Fast, good for PBMC/immune datasets
2. Compare with ScType: Alternative database-based method
3. Use CellTypist for complex datasets: ML-based, handles well
4. Manual refinement: Use direct annotation for corrections

Multi-Method Workflow

1. Run multiple annotation methods in parallel
2. Compare results for consensus
3. Manually refine discrepancies using direct annotation
4. Keep original cluster names for traceability

Tissue-Specific Annotation

1. Always specify tissue when using ScType/hitype
2. Use custom databases for non-standard tissues
3. Verify database contains relevant cell types

Reproducibility

1. Save cluster-to-celltype mapping (cluster2celltype.tsv)
2. Document which tool/database was used
3. Keep original cluster names using newcol or backup_col

External References

Tool Documentation

• ScType: https://github.com/IanevskiAleksandr/sc-type
• hitype: https://github.com/pwwang/hitype
• scCATCH: https://github.com/ZJUFanLab/scCATCH
• CellTypist: https://celltypist.org/

Database Downloads

• ScType databases:
  • Full: https://github.com/IanevskiAleksandr/sc-type/blob/master/ScTypeDB_full.xlsx
  • Short: https://github.com/IanevskiAleksandr/sc-type/blob/master/ScTypeDB_short.xlsx
• CellTypist models: https://celltypist.cog.sanger.ac.uk/models/models.json

Related Processes

• SeuratClustering: Clustering before annotation
• SeuratMap2Ref: Reference-based annotation (alternative)
• ClusterMarkers: Find markers for each cell type
• SeuratClusterStats: Visualize annotated clusters