Torbcellselection

[TOrBCellSelection]
cache = true  # Enable caching for this process

Input Specification

[TOrBCellSelection.in]
# Seurat object file (RDS/qs2 format) from SeuratClusteringOfAllCells
srtobj = ["SeuratClusteringOfAllCells"]

# Optional: Immune repertoire data file (RDS/qs2 format) from ScRepLoading
# Required unless ignore_vdj is set to true
immdata = ["ScRepLoading"]

Environment Variables

[TOrBCellSelection.envs]
# Whether to ignore VDJ information and use only marker gene expression
ignore_vdj = false

# Custom R expression to identify T/B cells
# Example: "Clonotype_Pct > 0.25" selects cells with >25% clonotype percentage
# Can use indicator genes: "Clonotype_Pct > 0.25 & CD3E > 0"
# If not provided, k-means clustering will be used
selector = null

# List of indicator genes for T/B cell identification
# For T cells: ["CD3E", "CD3D", "CD3G"] (positive markers)
#             or include negative markers: ["CD3E", "CD19", "CD14"]
# For B cells: ["CD19", "MS4A1", "CD79A", "CD79B"]
indicator_genes = ["CD3E"]

# Parameters for k-means clustering (if selector not provided)
# Reference: https://rdrr.io/r/stats/kmeans.html
# Note: dots in argument names should be replaced with hyphens
kmeans = {"nstart": 25}

Configuration Examples

Minimal Configuration (Default T Cell Markers)

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]

What this does: Uses default CD3E marker + k-means clustering with VDJ data to automatically select T cell clusters.

T Cell Selection with Multiple CD3 Markers

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Use all three CD3 markers for robust T cell identification
indicator_genes = ["CD3E", "CD3D", "CD3G"]

B Cell Selection (Default Markers)

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Select B cells using CD19 and CD20 (MS4A1) markers
indicator_genes = ["CD19", "MS4A1"]

Selection by Clonotype Percentage Threshold

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Select cells/clusters with >25% clonotype percentage as T/B cells
selector = "Clonotype_Pct > 0.25"

Selection Combined with Marker Expression

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Select cells with high clonotype percentage AND CD3E expression
indicator_genes = ["CD3E"]
selector = "Clonotype_Pct > 0.25 & CD3E > 0"

Selection Without VDJ Data (Markers Only)

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TOrBCellSelection.envs]
# Ignore VDJ data, use only marker gene expression
ignore_vdj = true

# Need at least 2 markers for k-means when VDJ is ignored
indicator_genes = ["CD3E", "CD3D", "CD3G"]

# First gene must be a positive marker for selection
# (CD3E is positive for T cells)

B Cell Selection Without VDJ Data

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TOrBCellSelection.envs]
# Select B cells using markers only (no VDJ data)
ignore_vdj = true
indicator_genes = ["CD19", "MS4A1", "CD79A"]

Custom K-means Parameters

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
indicator_genes = ["CD3E", "CD3D", "CD3G"]

# Custom k-means parameters
# nstart: number of random starts for stability (default: 25)
# iter.max: maximum iterations (default: 10 in R)
# Note: hyphens instead of dots in key names
kmeans = {"nstart": 50, "iter-max": 20}

Common Patterns

Pattern 1: Standard T Cell Selection (with VDJ)

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Robust T cell selection using all three CD3 markers
indicator_genes = ["CD3E", "CD3D", "CD3G"]

When to use: Typical TCR-seq analysis where T cells need to be separated from other cell types.

Pattern 2: Standard B Cell Selection (with VDJ)

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# B cell selection using CD19 and CD20 markers
indicator_genes = ["CD19", "MS4A1"]

When to use: BCR-seq analysis where B cells need to be separated from other cell types.

Pattern 3: High-Sensitivity T Cell Selection

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Lower threshold to capture more T cells
selector = "Clonotype_Pct > 0.10 & CD3E > 0"

When to use: When you suspect low-quality VDJ data or want to capture borderline T cells.

Pattern 4: High-Specificity T Cell Selection

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Higher threshold for clean T cell population
selector = "Clonotype_Pct > 0.50 & CD3E > 1"

When to use: When you want only the highest-confidence T cells (e.g., for clonal expansion analysis).

Pattern 5: Auto-Selection (K-means) with Multiple Markers

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Let k-means determine T cell clusters automatically
# No selector = automatic selection
indicator_genes = ["CD3E", "CD3D", "CD3G"]
kmeans = {"nstart": 50}

When to use: When you don't have a specific threshold in mind and want automatic unsupervised selection.

Dependencies

Upstream Processes

• SeuratClusteringOfAllCells: Provides clustered Seurat object with seurat_clusters metadata
• ScRepLoading: Provides VDJ data with clonotype information (unless ignore_vdj = true)

Downstream Processes

• SeuratClustering: Clusters the selected T/B cells for downstream analysis
• ScRepCombiningExpression: Combines selected cells with VDJ data
• ModuleScoreCalculator: Calculates module scores on selected cells
• Other TCR/BCR-specific processes (CDR3Clustering, TESSA, ClonalStats, etc.)

Workflow Integration

SeuratPreparing → SeuratClusteringOfAllCells → TOrBCellSelection → SeuratClustering → (downstream TCR/BCR analysis)
                                                       ↑
                                                ScRepLoading

Selection Methods Explained

Method 1: K-means Clustering (Default)

When selector is not provided, TOrBCellSelection performs:

1. Calculates average expression of indicator genes per cluster
2. If VDJ data available: calculates clonotype percentage per cluster
3. Performs k-means clustering (K=2) on [gene expressions + clonotype_pct]
4. Selects cluster with higher clonotype percentage (or higher expression of first indicator gene if no VDJ)

Pros: Automatic, unsupervised, adapts to data Cons: May select unexpected clusters if data is noisy

Method 2: Custom Selector Expression

Provide a custom R expression via selector:

• Can use any metadata column: Clonotype_Pct > 0.25
• Can combine with gene expression: Clonotype_Pct > 0.25 & CD3E > 0
• Can use complex logic: (Clonotype_Pct > 0.25 | CD3E > 1) & CD19 < 0.1

Pros: Full control, transparent selection criteria Cons: Requires domain knowledge, need to test thresholds

Method 3: Marker-Only Selection (ignore_vdj)

Set ignore_vdj = true to use only marker genes:

• Useful when VDJ data is poor or missing
• Requires at least 2 indicator genes for k-means
• First gene in list must be positive marker for the target cell type

Pros: Works without VDJ data, robust marker-based selection Cons: Requires good marker genes, may include non-clonal cells

Marker Gene Recommendations

T Cell Markers

Positive markers (expressed in T cells):

• CD3E: Core CD3 epsilon chain (most reliable)
• CD3D: Core CD3 delta chain
• CD3G: Core CD3 gamma chain

Negative markers (excluded from T cells):

• CD19: B cell marker
• MS4A1 (CD20): B cell marker
• CD14: Monocyte marker
• CD68: Macrophage marker

Recommended for T cells:

indicator_genes = ["CD3E", "CD3D", "CD3G"]

B Cell Markers

Positive markers (expressed in B cells):

• CD19: Pan-B cell marker (most reliable)
• MS4A1 (CD20): Mature B cell marker
• CD79A: B cell receptor component
• CD79B: B cell receptor component

Recommended for B cells:

indicator_genes = ["CD19", "MS4A1"]

Subtype-Specific Markers

For selecting specific T/B cell subtypes:

• T helper cells: CD4
• Cytotoxic T cells: CD8A, CD8B
• Regulatory T cells: FOXP3, IL2RA
• Memory B cells: CD27
• Plasma cells: CD38, SDC1 (CD138)

Validation Rules

Required Inputs

• srtobj must be specified (from SeuratClusteringOfAllCells)
• immdata required unless ignore_vdj = true

Marker Gene Validation

• Must provide at least 1 indicator gene
• If ignore_vdj = true, must provide at least 2 indicator genes
• First gene in indicator_genes must be a positive marker when using k-means without VDJ data

Selector Expression Validation

• selector must be a valid R expression
• Can reference: metadata columns (e.g., Clonotype_Pct), indicator genes (e.g., CD3E)
• Use R logical operators: & (and), | (or), ! (not)

K-means Parameter Validation

• kmeans must be a valid JSON object
• Valid keys: nstart, iter-max, algorithm, etc. (see stats::kmeans documentation)
• Dots in R argument names replaced with hyphens (e.g., iter.max → iter-max)

Troubleshooting

Issue: "No clonotype information found"

Cause: Barcode mismatch between scRNA-seq and VDJ data Solution:

1. Check barcode formats match in both datasets
2. Verify ScRepLoading processed VDJ data correctly
3. Try ignore_vdj = true to use marker genes only

Issue: "You need at least 2 markers to perform k-means clustering with VDJ data being ignored"

Cause: Using ignore_vdj = true with only 1 indicator gene Solution: Add more indicator genes or use a custom selector

Issue: Selected cells are not what I expected

Cause: K-means selected wrong cluster Solution:

1. Check the k-means plot in details/kmeans.png
2. Adjust indicator_genes to include more robust markers
3. Use custom selector instead of automatic selection
4. Adjust kmeans.nstart for more stable clustering (e.g., {"nstart": 50})

Issue: Too few or too many cells selected

Cause: Threshold too high or too low Solution:

1. Adjust selector threshold (e.g., Clonotype_Pct > 0.20 vs 0.30)
2. Review the selection table in details/data.txt
3. Check scatter plots in details/ directory for gene vs clonotype relationships

Issue: All cells selected as T cells (or none selected)

Cause: Poor VDJ data or incorrect marker genes Solution:

1. Verify VDJ data quality in ScRepLoading output
2. Check if CD3E is actually expressed in your data
3. Use ignore_vdj = true with robust marker genes
4. Manually inspect expression plots before running selection

Output Files

Primary Output

• outfile: Seurat object (qs2 format) containing only selected T/B cells
  • Located at: {{in.srtobj | stem}}.qs
  • Contains all original metadata + subset of cells

Detailed Output Directory (details/)

• data.txt: Table of indicator gene expression and clonotype percentage per cluster
  • Shows: Cluster, indicator gene expression, Clonotype_Pct, Cluster_Size, is_selected
• kmeans.png: K-means clustering visualization (if k-means used)
• selected_cells_per_sample.png: Bar plot of selected cells per sample
• selected_cells_pie.png: Pie chart of selected vs other cells
• selected-cells.png: Dimension plots showing VDJ data and selected cells
• feature-plots.png: Feature plots of indicator genes

Report

Interactive HTML report with visualization of selection results and cell composition.

Common Use Cases

Use Case 1: TCR-seq Analysis of PBMC Data

# Standard TCR-seq workflow
[SeuratClusteringOfAllCells]

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
indicator_genes = ["CD3E", "CD3D", "CD3G"]

[SeuratClustering]
# Clustering of selected T cells
[CDR3Clustering]
[TESSA]
[ClonalStats]

Use Case 2: BCR-seq Analysis of Tumor-Infiltrating Lymphocytes

[SeuratClusteringOfAllCells]

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]
immdata = ["ScRepLoading"]

[TOrBCellSelection.envs]
# Select B cells from TILs
indicator_genes = ["CD19", "MS4A1", "CD79A"]
selector = "CD19 > 0.5"

[SeuratClustering]
[CDR3Clustering]
[CellCellCommunication]

Use Case 3: RNA-only Data with T/B Cell Separation

[SeuratClusteringOfAllCells]

[TOrBCellSelection]
[TOrBCellSelection.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TOrBCellSelection.envs]
# No VDJ data, use markers only
ignore_vdj = true
indicator_genes = ["CD3E", "CD3D", "CD3G"]

[SeuratClustering]
[ScFGSEA]
[CellCellCommunication]

Key Notes

1. Not for Pure T/B Cell Populations: If all cells are already T or B cells, skip this process and use SeuratClustering directly.

2. Cluster-Level Selection: Selection happens at the cluster level, not single-cell level. All cells in selected clusters are kept.

3. Normalization: Gene expression values are normalized (mean=0, SD=1) before k-means clustering.

4. Marker First: When using k-means without VDJ data, the first indicator gene must be a positive marker for your target cell type.

5. Report Review: Always review the HTML report and plots in details/ to verify selection quality.

6. Threshold Tuning: Start with default k-means, then adjust to custom selector if automatic selection is not satisfactory.