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(obj@meta.data)
   # 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