Combining Contigs into Clones

There are varying definitions of clones in the literature. For the purposes of scRepertoire, we define a clone as cells with shared/trackable complementarity-determining region 3 (CDR3) sequences. Within this definition, one might use amino acid (aa) sequences of one or both chains to define a clone. Alternatively, we could use nucleotide (nt) or the V(D)JC genes (genes) to define a clone. The latter, genes, would be a more permissive definition of “clones,” as multiple amino acid or nucleotide sequences can result from the same gene combination. Another option to define a clone is the use of the V(D)JC and nucleotide sequence (strict). scRepertoire allows for the use of all these definitions of clones and enables users to select both or individual chains to examine.

The first step in getting clones is to use the single-cell barcodes to organize cells into paired sequences. This is accomplished using combineTCR() and combineBCR().

combineTCR

The combineTCR() function processes a list of TCR sequencing results, consolidating them to the level of individual cell barcodes. It handles potential issues with repeated barcodes by adding prefixes from samples and ID parameters. The output includes combined reads into clone calls by nucleotide sequence (CTnt), amino acid sequence (CTaa), VDJC gene sequence (CTgene), or a combination of nucleotide and gene sequence (CTstrict).

Key Parameter(s) for combineTCR()

input.data: A list of filtered contig annotations (e.g., filtered_contig_annotations.csv from 10x Cell Ranger) or outputs from loadContigs().
samples: Labels for your samples (recommended).
ID: Additional sample labels (optional).
removeNA: If TRUE, removes any cell barcode with an NA value in at least one chain (default is FALSE).
removeMulti: If TRUE, removes any cell barcode with more than two immune receptor chains (default is FALSE).
filterMulti: If TRUE, isolates the top two expressed chains in cell barcodes with multiple chains (default is FALSE).
filterNonproductive: If TRUE, removes non-productive chains if the variable exists in the contig data (default is TRUE).

To combine TCR contigs from contig_list and apply sample prefixes:

combined.TCR <- combineTCR(contig_list, 
                           samples = c("P17B", "P17L", "P18B", "P18L", 
                                            "P19B","P19L", "P20B", "P20L"),
                           removeNA = FALSE, 
                           removeMulti = FALSE, 
                           filterMulti = FALSE)

head(combined.TCR[[1]])

##                    barcode sample                     TCR1           cdr3_aa1
## 1  P17B_AAACCTGAGTACGACG-1   P17B       TRAV25.TRAJ20.TRAC        CGCSNDYKLSF
## 3  P17B_AAACCTGCAACACGCC-1   P17B TRAV38-2/DV8.TRAJ52.TRAC CAYRSAQAGGTSYGKLTF
## 5  P17B_AAACCTGCAGGCGATA-1   P17B      TRAV12-1.TRAJ9.TRAC     CVVSDNTGGFKTIF
## 7  P17B_AAACCTGCATGAGCGA-1   P17B      TRAV12-1.TRAJ9.TRAC     CVVSDNTGGFKTIF
## 9  P17B_AAACGGGAGAGCCCAA-1   P17B        TRAV20.TRAJ8.TRAC      CAVRGEGFQKLVF
## 10 P17B_AAACGGGAGCGTTTAC-1   P17B      TRAV12-1.TRAJ9.TRAC     CVVSDNTGGFKTIF
##                                                  cdr3_nt1
## 1                       TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT
## 3  TGTGCTTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGACATTT
## 5              TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT
## 7              TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT
## 9                 TGTGCTGTGCGAGGAGAAGGCTTTCAGAAACTTGTATTT
## 10             TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT
##                           TCR2          cdr3_aa2
## 1   TRBV5-1.None.TRBJ2-7.TRBC2    CASSLTDRTYEQYF
## 3  TRBV10-3.None.TRBJ2-2.TRBC2     CAISEQGKGELFF
## 5     TRBV9.None.TRBJ2-2.TRBC2 CASSVRRERANTGELFF
## 7     TRBV9.None.TRBJ2-2.TRBC2 CASSVRRERANTGELFF
## 9                         <NA>              <NA>
## 10    TRBV9.None.TRBJ2-2.TRBC2 CASSVRRERANTGELFF
##                                               cdr3_nt2
## 1           TGCGCCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC
## 3              TGTGCCATCAGTGAACAGGGGAAAGGGGAGCTGTTTTTT
## 5  TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 7  TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 9                                                 <NA>
## 10 TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
##                                                  CTgene
## 1         TRAV25.TRAJ20.TRAC_TRBV5-1.None.TRBJ2-7.TRBC2
## 3  TRAV38-2/DV8.TRAJ52.TRAC_TRBV10-3.None.TRBJ2-2.TRBC2
## 5          TRAV12-1.TRAJ9.TRAC_TRBV9.None.TRBJ2-2.TRBC2
## 7          TRAV12-1.TRAJ9.TRAC_TRBV9.None.TRBJ2-2.TRBC2
## 9                                  TRAV20.TRAJ8.TRAC_NA
## 10         TRAV12-1.TRAJ9.TRAC_TRBV9.None.TRBJ2-2.TRBC2
##                                                                                              CTnt
## 1                    TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT_TGCGCCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC
## 3  TGTGCTTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGACATTT_TGTGCCATCAGTGAACAGGGGAAAGGGGAGCTGTTTTTT
## 5  TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 7  TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 9                                                      TGTGCTGTGCGAGGAGAAGGCTTTCAGAAACTTGTATTT_NA
## 10 TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
##                                CTaa
## 1        CGCSNDYKLSF_CASSLTDRTYEQYF
## 3  CAYRSAQAGGTSYGKLTF_CAISEQGKGELFF
## 5  CVVSDNTGGFKTIF_CASSVRRERANTGELFF
## 7  CVVSDNTGGFKTIF_CASSVRRERANTGELFF
## 9                  CAVRGEGFQKLVF_NA
## 10 CVVSDNTGGFKTIF_CASSVRRERANTGELFF
##                                                                                                                                               CTstrict
## 1                           TRAV25.TRAJ20.TRAC;TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT_TRBV5-1.None.TRBJ2-7.TRBC2;TGCGCCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC
## 3  TRAV38-2/DV8.TRAJ52.TRAC;TGTGCTTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGACATTT_TRBV10-3.None.TRBJ2-2.TRBC2;TGTGCCATCAGTGAACAGGGGAAAGGGGAGCTGTTTTTT
## 5          TRAV12-1.TRAJ9.TRAC;TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TRBV9.None.TRBJ2-2.TRBC2;TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 7          TRAV12-1.TRAJ9.TRAC;TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TRBV9.None.TRBJ2-2.TRBC2;TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT
## 9                                                                                      TRAV20.TRAJ8.TRAC;TGTGCTGTGCGAGGAGAAGGCTTTCAGAAACTTGTATTT_NA;NA
## 10         TRAV12-1.TRAJ9.TRAC;TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT_TRBV9.None.TRBJ2-2.TRBC2;TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT

combineTCR() is the essential first step for organizing raw TCR sequencing data into a structured format for scRepertoire analyses. It allows for flexible handling of single and paired chains, barcode disambiguation, and initial filtering, producing a list of data frames where each row represents a single cell and its associated TCR clonotypes.

combineBCR

The combineBCR() function is the primary tool for processing raw B cell receptor contig data into a format ready for analysis. It is analogous to combineTCR() but includes specialized logic for handling the complexities of BCRs, such as somatic hypermutation. The function consolidates contigs into a single data frame per sample, with each row representing a unique cell.

By default (call.related.clones = TRUE), combineBCR() groups B cells into clones based on the similarity of their CDR3 sequences.

How `combineBCR()` Groups Related Clones

Internally calling clonalCluster() to build a network of related sequences.
Using the threshold parameter to define connections. The threshold is a normalized Levenshtein distance, where a value closer to 1.0 requires higher sequence similarity. The default of 0.85 is a good starting point.
Assigning a cluster-based ID to the CTstrict column.

Additionally, the group.by argument allows you to constrain the clustering analysis to only occur within distinct categories in your metadata. For example, using group.by = "sample" ensures that sequences from different samples are never compared or clustered together, even if they are identical.

Key Parameter(s) for combineBCR()

call.related.clones: If TRUE (default), uses clonalCluster() to identify related clones based on sequence similarity.
group.by: The column header used to group clones for clustering (if NULL, clusters will be calculated across all samples).
threshold: The similarity threshold for clonalCluster() (default: 0.85).
dist_type: The metric for calculating difference: “levenshtein” (default), “hamming”, “nw” (Needleman-Wunsch), or “sw” (Smith-Waterman).
dist_mat: The substitution matrix used if dist_type is “nw” or “sw” (e.g., “BLOSUM62”, “PAM30”)
normalize: How to handle the threshold: “none”, “length”, or “maxlen”.
chain: The chain to use for clustering when call.related.clones = TRUE (default: both).
sequence: The sequence type (nt or aa) for clustering (default: nt).
use.V, use.J: If TRUE, sequences must share the same V/J gene to be clustered (default: TRUE)
cluster.method: The clustering algorithm to apply to the edit-distanc network (default: components).

First, load the example BCR contig data:

# Load example BCR contig data
BCR.contigs <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv")

Then, combine BCR contigs using the default similarity clustering:

# Combine using the default similarity clustering
combined.BCR.clustered <- combineBCR(BCR.contigs, 
                                     samples = "Patient1", 
                                     threshold = 0.85)

# The CTstrict column contains cluster IDs (e.g., "cluster.1")
head(combined.BCR.clustered[[1]][, c("barcode", "CTstrict", "IGH", "cdr3_aa1")])

##                       barcode
## 1 Patient1_AAACCTGAGGGCACTA-1
## 2 Patient1_AAACCTGAGTACGTTC-1
## 3 Patient1_AAACCTGAGTCCGGTC-1
## 4 Patient1_AAACCTGCACCAGGTC-1
## 5 Patient1_AAACCTGGTAAATGAC-1
## 6 Patient1_AAACGGGTCACCTCGT-1
##                                                                                                                         CTstrict
## 1                                                                            NA_IGLV1-51.TGCGGAACATGGGATAGCAGCCTGAGTGCTGGCGTGTTC
## 2                           IGHV3-64.TGTGCGAAATCGTATAGCAGAGACCTGCCGCGGTACTTTGGCTCCTGG_IGKV3-15.TGTCAGCAGTATAGTAACTGGCCGCTCACTTTC
## 3                                                                                     NA_IGKV3-20.TGTCAACAGTATGGTGACTCTCTCCCTTTC
## 4 IGHV1-69-2.TGTGTGAGAGATGGGGCGGGTCACTATGGTTCGGGGATAGGCTACTACGGTATGGACGTCTGG_IGLV2-14.TGCAGTTCATATATAAGTACCAGCACTCTCGAGGTCCTATTC
## 5                  IGHV1-46.TGTTCGAGGGGTCGTCTCCCGATGCCAGCAGCTGGCAGTGGTTTGGTCTCCTGG_IGKV3-15.TGTCAGCACTATCATAACTGGCCTCCGTACACTTTT
## 6                                                                                  NA_IGKV3-11.TGTCAGCAGCGTAGCAACTGGCCTCTGACGTTC
##                              IGH              cdr3_aa1
## 1                           <NA>                  <NA>
## 2   IGHV3-64.IGHD6-6.IGHJ4.IGHG1      CAKSYSRDLPRYFGSW
## 3                           <NA>                  <NA>
## 4 IGHV1-69-2.IGHD3-10.IGHJ6.IGHM CVRDGAGHYGSGIGYYGMDVW
## 5  IGHV1-46.IGHD6-13.IGHJ5.IGHA2    CSRGRLPMPAAGSGLVSW
## 6                           <NA>                  <NA>

Advanced: Grouping by Alignment

For more biological accuracy—specifically when analyzing amino acid sequences—you can use alignment metrics. This allows the clustering to penalize conservative amino acid changes less than radical changes.

Here we use Needleman-Wunsch alignment with the BLOSUM80 substitution matrix:

combined.BCR.aligned <- combineBCR(BCR.contigs, 
                                   samples = "Patient1",
                                   sequence = "aa",        
                                   dist_type = "nw",      
                                   dist_mat = "BLOSUM80",  
                                   threshold = 0.85)

head(combined.BCR.aligned[[1]][, c("barcode", "CTstrict", "IGH", "cdr3_aa1")])

##                       barcode
## 1 Patient1_AAACCTGAGGGCACTA-1
## 2 Patient1_AAACCTGAGTACGTTC-1
## 3 Patient1_AAACCTGAGTCCGGTC-1
## 4 Patient1_AAACCTGCACCAGGTC-1
## 5 Patient1_AAACCTGGTAAATGAC-1
## 6 Patient1_AAACGGGTCACCTCGT-1
##                                                   CTstrict
## 1                                NA_IGLV1-51.CGTWDSSLSAGVF
## 2           IGHV3-64.CAKSYSRDLPRYFGSW_IGKV3-15.CQQYSNWPLTF
## 3                                   NA_IGKV3-20.CQQYGDSLPF
## 4 IGHV1-69-2.CVRDGAGHYGSGIGYYGMDVW_IGLV2-14.CSSYISTSTLEVLF
## 5        IGHV1-46.CSRGRLPMPAAGSGLVSW_IGKV3-15.CQHYHNWPPYTF
## 6                                  NA_IGKV3-11.CQQRSNWPLTF
##                              IGH              cdr3_aa1
## 1                           <NA>                  <NA>
## 2   IGHV3-64.IGHD6-6.IGHJ4.IGHG1      CAKSYSRDLPRYFGSW
## 3                           <NA>                  <NA>
## 4 IGHV1-69-2.IGHD3-10.IGHJ6.IGHM CVRDGAGHYGSGIGYYGMDVW
## 5  IGHV1-46.IGHD6-13.IGHJ5.IGHA2    CSRGRLPMPAAGSGLVSW
## 6                           <NA>                  <NA>

Filtering and Cleaning Data

combineBCR() includes several arguments to filter and clean the contig data during processing. * filterNonproductive = TRUE (Default): Removes any contigs that are not classified as productive, ensuring that only functional receptor chains are included in the analysis. * filterMulti = TRUE (Default): For cells with more than one heavy or light chain detected, this automatically selects the chain with the highest UMI count (read abundance) and discards the others. This helps resolve cellular multiplets or technical artifacts.

Here is an example applying these filters (though they are on by default):

cleaned.BCR <- combineBCR(BCR.contigs,
                          samples = "Patient1",
                          filterNonproductive = TRUE,
                          filterMulti = TRUE)

head(cleaned.BCR[[1]])

##                       barcode   sample                            IGH
## 1 Patient1_AAACCTGAGGGCACTA-1 Patient1                           <NA>
## 2 Patient1_AAACCTGAGTACGTTC-1 Patient1   IGHV3-64.IGHD6-6.IGHJ4.IGHG1
## 3 Patient1_AAACCTGAGTCCGGTC-1 Patient1                           <NA>
## 4 Patient1_AAACCTGCACCAGGTC-1 Patient1 IGHV1-69-2.IGHD3-10.IGHJ6.IGHM
## 5 Patient1_AAACCTGGTAAATGAC-1 Patient1  IGHV1-46.IGHD6-13.IGHJ5.IGHA2
## 6 Patient1_AAACGGGTCACCTCGT-1 Patient1                           <NA>
##                cdr3_aa1
## 1                  <NA>
## 2      CAKSYSRDLPRYFGSW
## 3                  <NA>
## 4 CVRDGAGHYGSGIGYYGMDVW
## 5    CSRGRLPMPAAGSGLVSW
## 6                  <NA>
##                                                          cdr3_nt1
## 1                                                            <NA>
## 2                TGTGCGAAATCGTATAGCAGAGACCTGCCGCGGTACTTTGGCTCCTGG
## 3                                                            <NA>
## 4 TGTGTGAGAGATGGGGCGGGTCACTATGGTTCGGGGATAGGCTACTACGGTATGGACGTCTGG
## 5          TGTTCGAGGGGTCGTCTCCCGATGCCAGCAGCTGGCAGTGGTTTGGTCTCCTGG
## 6                                                            <NA>
##                   IGLC       cdr3_aa2
## 1 IGLV1-51.IGLJ3.IGLC2  CGTWDSSLSAGVF
## 2  IGKV3-15.IGKJ4.IGKC    CQQYSNWPLTF
## 3  IGKV3-20.IGKJ4.IGKC     CQQYGDSLPF
## 4 IGLV2-14.IGLJ2.IGLC2 CSSYISTSTLEVLF
## 5  IGKV3-15.IGKJ2.IGKC   CQHYHNWPPYTF
## 6  IGKV3-11.IGKJ1.IGKC    CQQRSNWPLTF
##                                     cdr3_nt2
## 1    TGCGGAACATGGGATAGCAGCCTGAGTGCTGGCGTGTTC
## 2          TGTCAGCAGTATAGTAACTGGCCGCTCACTTTC
## 3             TGTCAACAGTATGGTGACTCTCTCCCTTTC
## 4 TGCAGTTCATATATAAGTACCAGCACTCTCGAGGTCCTATTC
## 5       TGTCAGCACTATCATAACTGGCCTCCGTACACTTTT
## 6          TGTCAGCAGCGTAGCAACTGGCCTCTGACGTTC
##                                                CTgene
## 1                             NA_IGLV1-51.IGLJ3.IGLC2
## 2    IGHV3-64.IGHD6-6.IGHJ4.IGHG1_IGKV3-15.IGKJ4.IGKC
## 3                              NA_IGKV3-20.IGKJ4.IGKC
## 4 IGHV1-69-2.IGHD3-10.IGHJ6.IGHM_IGLV2-14.IGLJ2.IGLC2
## 5   IGHV1-46.IGHD6-13.IGHJ5.IGHA2_IGKV3-15.IGKJ2.IGKC
## 6                              NA_IGKV3-11.IGKJ1.IGKC
##                                                                                                         CTnt
## 1                                                                 NA_TGCGGAACATGGGATAGCAGCCTGAGTGCTGGCGTGTTC
## 2                         TGTGCGAAATCGTATAGCAGAGACCTGCCGCGGTACTTTGGCTCCTGG_TGTCAGCAGTATAGTAACTGGCCGCTCACTTTC
## 3                                                                          NA_TGTCAACAGTATGGTGACTCTCTCCCTTTC
## 4 TGTGTGAGAGATGGGGCGGGTCACTATGGTTCGGGGATAGGCTACTACGGTATGGACGTCTGG_TGCAGTTCATATATAAGTACCAGCACTCTCGAGGTCCTATTC
## 5                TGTTCGAGGGGTCGTCTCCCGATGCCAGCAGCTGGCAGTGGTTTGGTCTCCTGG_TGTCAGCACTATCATAACTGGCCTCCGTACACTTTT
## 6                                                                       NA_TGTCAGCAGCGTAGCAACTGGCCTCTGACGTTC
##                                   CTaa
## 1                     NA_CGTWDSSLSAGVF
## 2         CAKSYSRDLPRYFGSW_CQQYSNWPLTF
## 3                        NA_CQQYGDSLPF
## 4 CVRDGAGHYGSGIGYYGMDVW_CSSYISTSTLEVLF
## 5      CSRGRLPMPAAGSGLVSW_CQHYHNWPPYTF
## 6                       NA_CQQRSNWPLTF
##                                                                                                                         CTstrict
## 1                                                                            NA_IGLV1-51.TGCGGAACATGGGATAGCAGCCTGAGTGCTGGCGTGTTC
## 2                           IGHV3-64.TGTGCGAAATCGTATAGCAGAGACCTGCCGCGGTACTTTGGCTCCTGG_IGKV3-15.TGTCAGCAGTATAGTAACTGGCCGCTCACTTTC
## 3                                                                                     NA_IGKV3-20.TGTCAACAGTATGGTGACTCTCTCCCTTTC
## 4 IGHV1-69-2.TGTGTGAGAGATGGGGCGGGTCACTATGGTTCGGGGATAGGCTACTACGGTATGGACGTCTGG_IGLV2-14.TGCAGTTCATATATAAGTACCAGCACTCTCGAGGTCCTATTC
## 5                  IGHV1-46.TGTTCGAGGGGTCGTCTCCCGATGCCAGCAGCTGGCAGTGGTTTGGTCTCCTGG_IGKV3-15.TGTCAGCACTATCATAACTGGCCTCCGTACACTTTT
## 6                                                                                  NA_IGKV3-11.TGTCAGCAGCGTAGCAACTGGCCTCTGACGTTC

combineBCR() is designed for processing B cell repertoire data, going beyond simple contig aggregation to incorporate advanced clustering based on CDR3 sequence similarity. This enables the identification of clonally related B cells, crucial for studying B cell development, affinity maturation, and humoral immune responses. Its filtering options further ensure the quality and interpretability of the processed data.

Next Steps

Additional Processing Steps - Add metadata, subset clones, bin by frequency, and export data.
Basic Clonal Visualizations - Visualize clonal abundance, length, and composition across samples.
Combining Clones and Single-Cell Objects - Attach clonal data to Seurat or SCE objects with combineExpression().

Compiled: April 03, 2026