Screpcombiningexpression

ScRepCombiningExpression Process Configuration

Purpose

Combine scTCR/BCR repertoire data with scRNA-seq expression data using scRepertoire::combineExpression(). This process integrates immune receptor information (CDR3 sequences, V(D)J genes, clonotypes) into a Seurat object's metadata, enabling clonotype-aware gene expression analysis.

When to Use

• When analyzing paired scTCR-seq + scRNA-seq data or paired scBCR-seq + scRNA-seq data
• Required for all downstream TCR/BCR analyses (CDR3Clustering, TESSA, ClonalStats)
• After ScRepLoading and SeuratClustering (or TOrBCellSelection)
• When you need to visualize or analyze clonotype distribution across RNA clusters

Configuration Structure

Process Enablement

[ScRepCombiningExpression]
cache = true

Input Specification

[ScRepCombiningExpression.in]
# Required inputs
screpfile = ["ScRepLoading"]      # scRepertoire object from ScRepLoading
srtobj = ["SeuratClustering"]     # Seurat object with RNA data

Note: Barcodes in srtobj must match barcodes in screpfile. Ensure both datasets were generated from the same cell calling run.

Environment Variables

[ScRepCombiningExpression.envs]
# Clonotype definition
cloneCall = "aa"  # How to define a clonotype (default: aa)

# Chain selection
chain = "both"    # Which TCR/BCR chains to use (default: both)

# Frequency calculation
group_by = "Sample"  # Group for frequency/proportion calculation (default: Sample)
proportion = true    # Use proportion (true) or total frequency (false) (default: true)

# Filtering options
filterNA = false   # Remove cells without TCR/BCR data (default: false)

# Clone size bins
cloneSize = {Rare = 0.0001, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1}

# Label customization
addLabel = false   # Add label to frequency header (default: false)

Environment Variables Explained

cloneCall: Defines clonotype grouping strategy

• gene: Group by V(D)JC gene usage (e.g., TRBV12-301, TRBJ2-701)
• nt: Group by CDR3 nucleotide sequence
• aa: Group by CDR3 amino acid sequence (default)
• strict: Group by V(D)JC gene + CDR3 nucleotide (most specific)
• Custom: Use a custom column from the data

chain: Which receptor chains to include

• both: Include both chains (e.g., TRA + TRB for TCR, IGH + IGL/IGK for BCR)
• TRA: T cell receptor alpha chain only
• TRB: T cell receptor beta chain only
• TRG: T cell receptor gamma chain only
• TRD: T cell receptor delta chain only
• IGH: B cell immunoglobulin heavy chain only
• IGL: B cell immunoglobulin light chain (lambda/kappa) only

group_by: Column for frequency calculation

• "Sample": Calculate clonotype frequency per sample (default)
• "seurat_clusters": Calculate per RNA cluster
• NULL or "none": Keep input format without grouping
• Custom: Use any metadata column name

proportion:

• true: Calculate as proportion (0-1 scale) within group
• false: Calculate as absolute frequency (counts)

filterNA:

• true: Remove cells without V(D)J data from Seurat object
• false: Keep all cells, add VDJ_Presence = FALSE for non-productive cells

cloneSize: Bins for categorizing clone sizes

• Values are thresholds for proportion or frequency
• Keys become new metadata column with categories
• If proportion = false, upper limit may auto-adjust based on data

addLabel: Useful when running multiple configurations with different group_by or cloneCall settings.

Configuration Examples

Minimal Configuration

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]

Use when: Default CDR3aa clonotype definition is sufficient.

Standard TCR Integration

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]
[ScRepCombiningExpression.envs]
# Use CDR3 amino acid for clonotype definition
cloneCall = "aa"
# Include both TRA and TRB chains
chain = "both"
# Calculate frequency per sample
group_by = "Sample"
# Use proportional frequency
proportion = true

Use when: Analyzing TCR data with standard parameters.

BCR Heavy+Light Chain Integration

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]
[ScRepCombiningExpression.envs]
# BCR data - analyze both heavy and light chains
chain = "both"
# Use V gene + CDR3aa for specific clonotype definition
cloneCall = "aa"
# Calculate frequency per sample
group_by = "Sample"

Use when: Analyzing paired IGH + IGL/IGK BCR data.

Clonotype by RNA Cluster

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]
[ScRepCombiningExpression.envs]
# Calculate clonotype frequency within each RNA cluster
group_by = "seurat_clusters"
# Use absolute counts instead of proportions
proportion = false

Use when: Need to track which RNA clusters contain expanded clones.

Remove Non-Productive Cells

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]
[ScRepCombiningExpression.envs]
# Remove cells without productive V(D)J rearrangements
filterNA = true

Use when: Analysis should only include cells with receptor data.

Gene-Based Clonotype Definition

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]
[ScRepCombiningExpression.envs]
# Group by V(D)JC gene usage only
cloneCall = "gene"

Use when: Interested in V gene bias rather than CDR3 specificity.

Common Patterns

Pattern 1: Simple TCR Addition (Default)

[ScRepCombiningExpression]
[ScRepCombiningExpression.in]
screpfile = ["ScRepLoading"]
srtobj = ["SeuratClustering"]

Best for: Initial exploratory analysis with default CDR3aa clonotypes.

Pattern 2: TCR Beta Chain Only

[ScRepCombiningExpression.envs]
# Analyze only TRB chain
chain = "TRB"

Best for: TRB-focused analyses when TRA data is noisy or unavailable.

Pattern 3: BCR with Custom Clone Bins

[ScRepCombiningExpression.envs]
chain = "both"
cloneCall = "aa"
# Custom bins for BCR clonal expansion
cloneSize = {Single = 1, Small = 3, Medium = 10, Large = 50, Hyperexpanded = 500}

Best for: BCR analysis where expansion patterns differ from TCR.

Pattern 4: Frequency by Condition

[ScRepCombiningExpression.envs]
# Calculate frequency per experimental condition
group_by = "Diagnosis"
# Use absolute counts
proportion = false

Best for: Comparing clonal expansion across treatment groups.

Pattern 5: Strict Clonotype Definition

[ScRepCombiningExpression.envs]
# Most specific: V(D)JC gene + CDR3 nucleotide
cloneCall = "strict"

Best for: High-resolution clonotype analysis where gene + CDR3 matter.

Metadata Added to Seurat Object

After running ScRepCombiningExpression, the Seurat object's metadata will include:

Core Columns (from scRepertoire)

• CTgene: V(D)JC gene names (e.g., "TRBV12-301_TRBJ2-701" for paired chains)
• CTnt: CDR3 nucleotide sequences
• CTaa: CDR3 amino acid sequences (separated by _ for paired chains)
• CTcount: Count of cells in each clonotype
• CTfrequency: Frequency of clonotype within group (if group_by set)
• CTproportion: Proportion of clonotype within group (if group_by set)
• cloneSize: Category from cloneSize bins (Rare, Small, Medium, Large, Hyperexpanded)

Custom Column (immunopipe-specific)

• VDJ_Presence: Boolean indicating if cell has TCR/BCR sequence
  • TRUE: Productive V(D)J rearrangement detected
  • FALSE: No productive V(D)J data

Chain-Specific Columns (when applicable)

• TRA_1, TRA_2: Individual CDR3aa sequences for TRA chains
• TRB_1, TRB_2: Individual CDR3aa sequences for TRB chains
• IGH_1, IGH_2: Individual CDR3aa sequences for IGH chains
• IGL_1, IGL_2: Individual CDR3aa sequences for IGL/IGK chains

Dependencies

Upstream Processes

• ScRepLoading (required): Loads TCR/BCR data from raw files
• SeuratClustering or TOrBCellSelection (required): Provides RNA data with clustering

Downstream Processes

• CDR3Clustering: Clusters TCR/BCR clones by CDR3 similarity
• TESSA: TCR-specific analysis using clonotype information
• ClonalStats: Visualizes clonal statistics and diversity
• CDR3AAPhyschem: Analyzes CDR3 physicochemical properties
• SeuratClusterStats: Uses combined metadata for cluster statistics

Data Flow

ScRepLoading (TCR/BCR data)
         ↓
SeuratClustering (RNA clusters)
         ↓
ScRepCombiningExpression (integrate)
         ↓
CDR3Clustering / TESSA / ClonalStats

Validation Rules

Barcode Matching

• Critical: Cell barcodes in screpfile must exactly match barcodes in srtobj
• Common cause: Different cell calling algorithms or filtering thresholds
• Solution: Re-run cell calling with same parameters or manually filter to common barcodes

Clonotype Definition Constraints

• cloneCall = "strict" requires both V(D)J genes AND CDR3 nt to match
• cloneCall = "gene" only uses gene usage (may over-group clones)
• cloneCall = "aa" is recommended for most analyses (balance specificity and grouping)

Chain Selection Rules

• chain = "both" requires paired data (TRA+TRB for TCR, IGH+IGL/IGK for BCR)
• If single-chain data, specify which chain: chain = "TRB" or chain = "IGH"
• Unpaired chains will result in NA values in CTaa column

Troubleshooting

Issue: No metadata columns added

Possible causes:

1. Barcode mismatch between RNA and TCR/BCR data
2. All cells filtered out (low QC or missing V(D)J data)
3. Empty TCR/BCR input file

Diagnosis:

# Check barcode overlap
length(intersect(Cells(srtobj), rownames(screpfile)))

Solution: Ensure both datasets come from same cell calling run, or manually filter to common barcodes.

Issue: Many cells with VDJ_Presence = FALSE

Possible causes:

1. Low sequencing depth for V(D)J library
2. Stringent V(D)J calling filters
3. Non-T/B cells in dataset (e.g., myeloid cells)

Solution:

• Check V(D)J library quality metrics
• Verify cell type composition
• Consider filterNA = true to remove non-productive cells

Issue: Empty CTaa column

Possible causes:

1. Wrong chain selected (e.g., chain = "TRB" for alpha-only T cells)
2. Paired chain data but single-chain selection
3. Non-productive rearrangements filtered out

Solution:

• Use chain = "both" if paired data available
• Verify which chains are present in raw contig file
• Check ScRepLoading output for chain distribution

Issue: Clonotype frequency doesn't sum to 1

Possible causes:

1. group_by includes cells without V(D)J data
2. proportion = false (using counts, not proportions)
3. Multiple samples with different cell counts

Solution:

• Check if filterNA = true should be enabled
• Verify group_by column is appropriate
• Use proportion = true for normalized frequencies

Issue: All clones in "Rare" category

Possible causes:

1. cloneSize thresholds too high for data
2. Low clonal expansion in dataset
3. proportion = false with absolute counts

Solution:

• Adjust cloneSize bins to match data distribution
• Check if dataset truly has expanded clones
• Use proportion = true for relative clone sizes

Best Practices

Data Preparation

1. Run same cell calling for RNA and V(D)J libraries
2. Apply consistent QC filters before integration
3. Verify sample matching between RNA and TCR/BCR metadata

Parameter Selection

1. Use cloneCall = "aa" for most analyses (default)
2. Use chain = "both" for paired receptor data
3. Set group_by = "Sample" for frequency calculations (default)
4. Keep filterNA = false to distinguish productive vs non-productive cells

Validation

1. Check barcode overlap before running: sum(rownames(srtobj) %in% rownames(screpfile))
2. Verify VDJ_Presence distribution: table(srtobj$VDJ_Presence)
3. Inspect clone size distribution: table(srtobj$cloneSize)
4. Check clonotype counts: length(unique(srtobj$CTaa))

External References

scRepertoire Documentation

• combineExpression: https://www.borch.dev/uploads/screpertoire/reference/combineexpression
• combineTCR/combineBCR: https://www.borch.dev/uploads/screpertoire/articles/combining_contigs
• Working with Single-Cell Objects: https://www.borch.dev/uploads/screpertoire/articles/attaching_sc

Clonotype Definition

• CDR3aa: Most common - uses amino acid sequence of CDR3 region
• CDR3nt: More specific - uses nucleotide sequence (higher resolution)
• V(D)JC gene: Broader - groups by gene usage only
• Strict: Most specific - requires both gene usage and CDR3nt match

Chain Pairing

• TCR: Typically TRA (alpha) + TRB (beta) paired
• BCR: IGH (heavy) + IGL/IGK (light) paired
• Gamma-delta T cells: TRG + TRD chains