Reconstructs B-cell clonal families, quantifies somatic hypermutation and selection, and builds antibody lineage trees with the Immcantation R suite (alakazam, shazam, scoper, dowser, tigger) on AIRR-format BCR data. Use when deriving the clonal-clustering threshold from the distToNearest bimodal valley (never a hardcoded 0.15); choosing hierarchicalClones vs spectralClones (vj vs novj) for SHM-diverged repertoires; personalizing the germline with TIGGER before mutation counting; reconstructing D-masked germlines with createGermlines; measuring R/S mutation frequency by CDR and FWR region; testing antigen-driven selection with BASELINe; comparing Hill-number diversity at equal sampling depth; and inferring IgPhyML lineage trees for affinity maturation, class-switch, and ancestral-antibody analysis.
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Reconstructs B-cell clonal families, quantifies somatic hypermutation and selection, and builds antibody lineage trees with the Immcantation R suite (alakazam, shazam, scoper, dowser, tigger) on AIRR-format BCR data. Use when deriving the clonal-clustering threshold from the distToNearest bimodal valley (never a hardcoded 0.15); choosing hierarchicalClones vs spectralClones (vj vs novj) for SHM-diverged repertoires; personalizing the germline with TIGGER before mutation counting; reconstructing D-masked germlines with createGermlines; measuring R/S mutation frequency by CDR and FWR region; testing antigen-driven selection with BASELINe; comparing Hill-number diversity at equal sampling depth; and inferring IgPhyML lineage trees for affinity maturation, class-switch, and ancestral-antibody analysis.
tool_type
r
primary_tool
alakazam
Version Compatibility
Reference examples tested with: alakazam 1.3+, shazam 1.2+, scoper 1.3+, dowser 2.x, tigger 1.1+ (Immcantation R suite), plus IgBLAST, Change-O, and PHYLIP/IgPhyML as external dependencies.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Note: createGermlines now lives in dowser (not shazam); BASELINe selection uses / (the old name is gone); mutation R/S classification is set by , not a fake (that has no member); the clonal threshold must come from , never a literature constant.
calcBaseline
groupBaseline
estimateBaseline
regionDefinition
mutationDefinition=MUTATION_SCHEMES$S5F
S5F
findThreshold
Immcantation Analysis
"Find the B-cell clones and measure their affinity maturation" -> partition SHM-diverged sequences into clonal families, quantify somatic hypermutation and selection against a reconstructed germline, and build antibody lineage trees.
The governing principle: the clonal threshold is derived, not assumed
Every downstream number in a BCR analysis -- clone counts, diversity, selection strength, tree topology -- inherits its error from one quantity: the nucleotide-distance cutoff used to group sequences into clonal families. That cutoff is NOT a literature constant. distToNearest computes each sequence's Hamming distance to its nearest neighbor within the same V gene, J gene, and junction length; because unrelated rearrangements almost never share V/J plus a near-identical junction by chance while clonally related sequences differ only by SHM, the resulting dist_nearest distribution is bimodal. findThreshold locates the VALLEY between the clonally-related mode (small distances) and the unrelated mode (large distances). That valley is the per-dataset threshold. A hardcoded threshold = 0.15 is the exact anti-pattern to avoid: the valley shifts with subject, locus, sequencing depth, and chemistry, and a wrong threshold silently merges independent lineages or shatters one clone into many (Gupta 2015 Bioinformatics 31:3356; Nouri 2018 Bioinformatics 34:i341).
If the dist_nearest histogram is UNIMODAL (no clear valley), a fixed threshold is undefined -- switch to spectralClones(method="novj"), whose adaptive local threshold does not require findThreshold.
Why BCR needs a different clonotype definition than TCR
TCR does not hypermutate, so all progeny of a founding T cell share the exact CDR3 nucleotide sequence and exact-CDR3 matching is correct. BCR hypermutates: members of one lineage are NOT identical, so exact-CDR3 shatters a single clone into hundreds of fragments. The field-standard BCR clone groups sequences sharing the same V gene, same J gene, and same junction LENGTH, then clusters within that partition by junction nucleotide distance at the derived threshold. Use nucleotide (not amino-acid) junction distance -- SHM is a nucleotide process and codon degeneracy would blur it.
Method
How it clusters
Best when
Fails when
hierarchicalClones
Single-linkage on junction Hamming distance within V/J/length partitions, cut at the findThreshold value
dist_nearest is clearly bimodal; a defensible fixed threshold exists
Unimodal distance histogram (threshold undefined); heavily diverged clones fragment
spectralClones(method="novj")
Spectral clustering with an adaptive local junction-similarity threshold; no fixed cutoff needed
Unimodal repertoires where no findThreshold valley exists
Very small groups (spectral needs several sequences)
spectralClones(method="vj")
Adds shared V/J SHM (targeting model) to junction homology
SHM-driven within-clone divergence pulls junctions apart; a mutated clone would otherwise be split
Needs germline_alignment/sequence_alignment and is slower
Verify current best practice against the SCOPer vignette before committing to a method; the spectral vj model is the reason spectral clustering holds diverged clones together where a fixed threshold fragments them.
Pipeline order (load-bearing)
This order is not interchangeable; getting it wrong silently corrupts mutation and selection counts.
TIGGER genotype FIRST. An unrecorded personal germline polymorphism otherwise reads as recurrent SHM at a fixed position -- it inflates mutation and selection counts AND adds spurious junction distance that corrupts distToNearest.
createGermlines (per-sequence) to reconstruct the D-masked germline BEFORE any mutation counting (mutation = observed vs inferred germline).
distToNearest -> findThreshold to derive the threshold.
createGermlines again per-clone (clone consensus germline), then observedMutations with the CDR3/junction MASKED (the D-masked germline handles this; junctional N/P bases have no template).
BASELINe selection (calcBaseline -> groupBaseline) with a codon+motif-aware null -- raw R/S is biased by germline codon structure and SHM hotspot/transition bias, so naive R/S is not selection.
Dowser lineage trees.
Immcantation reads and writes one AIRR TSV. Expected columns: sequence_id, v_call, j_call, junction, junction_length, sequence_alignment, germline_alignment_d_mask, clone_id (plus locus and cell_id for single-cell). These are lowercase snake_case; legacy UPPERCASE Change-O names (V_CALL, JUNCTION, CLONE) are deprecated and mixing schemas is a silent failure.
Personalize the germline with TIGGER
Goal: Build the subject's own V-gene genotype so germline polymorphisms are not miscounted as somatic mutations.
Approach: Detect novel alleles from the mutation-frequency-vs-position signature, infer the personal genotype, and re-call V alleles against it before anything downstream.
library(tigger)
ighv <- readIgFasta('IMGT_Human_IGHV.fasta')# named vector of germline V alleles
novel <- findNovelAlleles(db, germline_db = ighv, v_call ='v_call', nproc =1)
genotype <- inferGenotypeBayesian(db, germline_db = ighv, novel = novel, find_unmutated =TRUE)
gt_seqs <- genotypeFasta(genotype, germline_db = ighv, novel = novel)
db <- reassignAlleles(db, genotype_db = gt_seqs)# collapse ambiguous calls to alleles the subject carries
Derive the clonal threshold
Goal: Obtain the per-dataset nucleotide-distance cutoff that separates clonally related from unrelated sequences.
Approach: Compute each sequence's distance to its nearest same-V/J/length neighbor, then find the valley of the bimodal distribution. Inspect the histogram before trusting the value.
library(shazam)
db <- distToNearest(db, sequenceColumn ='junction', vCallColumn ='v_call',
jCallColumn ='j_call', model ='ham', normalize ='len', nproc =1)# Single-cell: add cellIdColumn='cell_id', locusColumn='locus', onlyHeavy=TRUE# (light chains lack the junction diversity to define clones alone)
thr_obj <- findThreshold(db$dist_nearest, method ='density')# 'gmm' makes the FP/FN tradeoff explicit
threshold <- thr_obj@threshold # S4 slot; NA/unimodal -> use spectralClones('novj')
plot(thr_obj)# confirm bimodality before proceeding
Cluster sequences into clonal families
Goal: Group SHM-diverged sequences descended from one naive B cell into clones.
Approach: Cluster within V/J/junction-length partitions at the derived threshold; for single-cell paired data, cluster on heavy chains, then resolve light chains as a separate step.
library(scoper)
results <- hierarchicalClones(db, threshold = threshold, method ='nt', linkage ='single')
db <- as.data.frame(results)# adds clone_id# Single-cell paired BCR: cluster on heavy only, then split clones by light-chain V/J.# The scoper only_heavy/split_light args are DEPRECATED; use dowser::resolveLightChains:# db <- dowser::resolveLightChains(db)# Unimodal repertoire (no clear threshold): adaptive, SHM-aware alternative# db <- as.data.frame(spectralClones(db, method = 'vj',# germline = 'germline_alignment', sequence = 'sequence_alignment'))
Reconstruct germline and quantify SHM
Goal: Measure somatic hypermutation as replacement (R) and silent (S) frequency by region, the signal of affinity maturation.
Approach: Rebuild the D-masked clonal germline, then compare each observed V-region to it. Use frequency (not raw counts) when coverage varies, and restrict to the V segment so the untemplated junction is excluded.
library(dowser)
references <- readIMGT('imgt/human/vdj')# IMGT-gapped V/D/J reference dir
db <- createGermlines(db, references)# per-clone germline; adds germline_alignment_d_mask
db <- observedMutations(db, sequenceColumn ='sequence_alignment',
germlineColumn ='germline_alignment_d_mask',
regionDefinition = IMGT_V,# V only; stops before CDR3/junction
frequency =TRUE, nproc =1)# Adds mu_freq_cdr_r, mu_freq_cdr_s, mu_freq_fwr_r, mu_freq_fwr_s# For property-based R/S use mutationDefinition = CHARGE_MUTATIONS (or HYDROPATHY/POLARITY/VOLUME).# S5F is a TARGETING model (HH_S5F) for selection, NOT a mutationDefinition.
Test for selection (BASELINe)
Goal: Decide whether replacement mutations are enriched (positive selection, typically CDR) or depleted (purifying, typically FWR) beyond what SHM alone produces.
Approach: Compute the expected R/S per region from the germline under an SHM targeting model, form a posterior over selection strength per sequence, then convolve posteriors within groups. Analyze one representative per clone so shared ancestral mutations are not double-counted.
Goal: Compare clonal diversity across samples without confounding by sequencing depth.
Approach: Report a Hill-number profile with uniform resampling to equal N and bootstrap CIs; comparing raw diversity across unequal-depth libraries measures depth, not biology.
library(alakazam)
div <- alphaDiversity(db, group ='sample_id', clone ='clone_id',
min_q =0, max_q =2, step_q =0.1,# q=0 richness, q=1 Shannon, q=2 Simpson
ci =0.95, nboot =200)# uniform=TRUE (default) resamples to equal N
plot(div)
Build lineage trees
Goal: Reconstruct each clone's antibody lineage to trace affinity maturation, class switching, and ancestral (intermediate) antibodies.
Approach: Build clonally-collapsed, germline-rooted trees under IgPhyML's HLP codon model, which encodes SHM's context-dependence, non-reversibility, and known germline root -- assumptions that standard phylogenetics violates.
clones <- formatClones(db, traits ='c_call', minseq =3)# collapse duplicates, attach clonal germline
trees <- getTrees(clones, build ='igphyml',
igphyml ='/usr/local/share/igphyml/src/igphyml', nproc =1)
plots <- plotTrees(trees)# ggtree, germline-rooted; color tips by trait# findSwitches(clones, ...) + testSP/testSC reconstruct isotype/tissue switching across bootstrap trees.# Legacy: alakazam::buildPhylipLineage() (PHYLIP dnapars max-parsimony) still exists but is superseded.
Common Errors
Symptom
Cause
Fix
Clone counts differ wildly from a published study
Hardcoded threshold = 0.15 instead of the data's valley
Run distToNearest -> findThreshold; read @threshold; inspect the histogram
observedMutations gives near-zero or nonsensical mutations
Counted before createGermlines (no reconstructed germline)
Run createGermlines first; compare against germline_alignment_d_mask
Inflated R mutations concentrated in CDR3
Junction/CDR3 not masked; junctional N/P bases have no template
Use the D-masked germline and regionDefinition = IMGT_V (V only)
MUTATION_SCHEMES$S5F errors or gives odd R/S
No S5F member exists; S5F is a targeting model, not a mutation definition
Drop it (default R/S by AA identity) or use CHARGE_MUTATIONS; use HH_S5F only as a targeting model
estimateBaseline not found
Renamed
Use calcBaseline then groupBaseline/testBaseline
Recurrent "mutation" at the same position across many sequences
Unrecorded personal germline allele scored as SHM
Run TIGGER (findNovelAlleles/inferGenotypeBayesian/reassignAlleles) before germline reconstruction
Diversity differences vanish or invert after resequencing
Compared raw diversity across unequal-depth samples
Use alphaDiversity with uniform resampling (default) and bootstrap CIs
Same clone appears in two individuals
Pooled clones across subjects with private genotypes
Cluster clones within each subject; treat cross-subject sharing as a separate convergence question
Unimodal dist_nearest histogram, findThreshold returns NA
No clear valley (e.g. low-SHM or shallow repertoire)
Use spectralClones(method = 'novj') (adaptive threshold)
Related Skills
mixcr-analysis - Produce AIRR/clonotype input for BCR
scirpy-analysis - Single-cell BCR integration and handoff
Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 2015, 31(20):3356-3358.
Vander Heiden JA, Yaari G, Uduman M, Stern JNH, O'Connor KC, Hafler DA, Vigneault F, Kleinstein SH. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 2014, 30(13):1930-1932.
Yaari G, Uduman M, Kleinstein SH. Quantifying selection in high-throughput immunoglobulin sequencing data sets (BASELINe). Nucleic Acids Research 2012, 40(17):e134.
Yaari G, Vander Heiden JA, Uduman M, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data (S5F). Frontiers in Immunology 2013, 4:358.
Gadala-Maria D, Yaari G, Uduman M, Kleinstein SH. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles (TIGGER). PNAS 2015, 112(8):E862-E870.
Nouri N, Kleinstein SH. A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data (SCOPer). Bioinformatics 2018, 34(13):i341-i349.
Hoehn KB, Pybus OG, Kleinstein SH. Phylogenetic analysis of migration, differentiation, and class switching in B cells (Dowser). PLoS Computational Biology 2022, 18(4):e1009885.
Hoehn KB, Lunter G, Pybus OG. A phylogenetic codon substitution model for antibody lineages (IgPhyML). Genetics 2017, 206(1):417-427.
Stern JNH, Yaari G, Vander Heiden JA, et al. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Science Translational Medicine 2014, 6(248):248ra107.
