Threadfin

Relationships between B-cell clones from paired single-cell RNA and receptor sequencing

Threadfin is a Python package for paired single-cell RNA and B-cell receptor (BCR) sequencing. It uses the clone — the B cells that descend from one V(D)J rearrangement — as the unit of analysis. Each clone is represented by the distribution of transcriptional states among its sampled cells, estimated relative to the cells sampled alongside it and shrunk according to the number of cells captured. Clones are then compared with one another: in a clone embedding with one point per clone, in clonal programmes of clones that behave alike, and in clone-level tests of anything you measured. Threadfin works with AnnData and Scanpy, and defines clones with Dandelion.

Why clones?

The cells of one clone are distributed across states: germinal-centre (GC) B cells, plasmablasts, plasma cells and memory B cells. Two clones can have the same average state and still differ in how their cells are distributed. Selection, antigen affinity and fate bias act on clones, so questions such as which clones connect the germinal centre with plasma-cell output or does antigen binding go with a clone's state need the clone as the unit of representation and of statistical inference. Three features of repertoires sampled in vivo make this difficult, and Threadfin addresses each:

• Most clones are captured as one or a few cells. Profiles are shrunk according to the number of cells sampled, and each clone carries a reliability score. No clone is discarded for being small.
• Clone membership is confounded with donor, sample, tissue and sorting gate. Each clone is described relative to the cells sampled with it, and every test shuffles labels within the sampling design.
• Treating cells as independent inflates significance. Tests use clones, not cells, as replicates.

What Threadfin answers

Examples from public data

The manuscript applies Threadfin to nine paired datasets from immunization, infection, vaccination and spontaneous germinal centres (figures); scripts and results for these and further public datasets are in case studies, and everything behind the manuscript — status, datasets, every analysis and a benchmark against related tools — is in manuscript/.

• Gut-associated lymphoid tissues (Siniscalco et al., Immunity 2025): the clone embedding resolves a continuous path of clones from GC B cells, through clones that contain both GC B cells and plasma cells, to plasma-cell clones, with clones that lack GC-fate labelling grouped at its end.
• Spontaneous germinal centres (Castrillon et al., eLife 2023): position along the clone embedding orders clones by the diversity of their cell states, and memory-containing clones concentrate at one end.
• Plasmodium infection (Skinner et al., Nat. Immunol. 2026): whether plasmablasts were sampled together with GC B cells of the same clone is a property of clones that cannot be read from the cell-level embedding.
• Model-antigen germinal centres (Merkenschlager et al., Nature 2025): the main axis of variation between clone profiles follows recent division in two cohorts whose sequencing libraries are crossed, so the association cannot be a library artefact; antigen-bait and dark-zone gates are also associated with clone profiles but were each sequenced in their own library.

Installation

pip install "git+https://github.com/Reynold4k/Threadfin.git"
# with Dandelion, to define clones from BCR contigs:
pip install "threadfin[dandelion] @ git+https://github.com/Reynold4k/Threadfin.git"

Threadfin requires Python ≥ 3.10 and is tested on 3.10 to 3.14, down to the oldest dependency versions it allows (compatibility); Scanpy, Leiden, UMAP and Harmony are installed with it, and no GPU or R is needed. Clones can be defined from BCR contigs with Dandelion (the [dandelion] extra also pins matplotlib<3.9, which Dandelion 0.5.7 needs but does not declare), or you can provide your own clone ids in adata.obs["clone_id"]. Dandelion's clone calls depend on Python's string-hash seed; start Python with PYTHONHASHSEED=0 if clone ids must be identical run after run.

Quick start

import scanpy as sc
import threadfin as tf

adata = sc.read_h5ad("b_cells.h5ad")           # raw counts, one row per cell

result = tf.run(
    adata,
    bcr="filtered_contig_annotations.csv",     # Cell Ranger VDJ output or an AIRR .tsv
    donor_key="donor",                         # the individual each cell came from
    sample_key="sample",                       # the sample or library each cell came from
)
print(result.summary())                        # plain-language report
result.plot("threadfin_overview.pdf")          # clone map, coherence, stability, composition
print(result.methods())                        # a Methods paragraph with the settings used

tf.run defines clones within each donor, builds an expression embedding without immunoglobulin genes, estimates one profile per clone, tests clonal coherence, lays out the clone embedding, finds clonal programmes and reports their stability. Add test=[...] to test your own labels and time_key= to ask whether clones keep their state, and n_jobs=-1 to use every CPU core (the results do not change). No data at hand? tf.sim.simulate_repertoire() makes a repertoire with a known answer (Tutorial 1). T cells work the same way, with TCR contigs as bcr, and receptor data loaded with scirpy can be passed as they are (Tutorial 2, section 6).

A complete, tested tutorial on public data

docs/tutorials/05_end_to_end_tutorial.ipynb takes you from an empty folder to the main result of the manuscript's Figure 4: it creates the environment (environment.yml), downloads a published dataset from GEO (GSE287123, about 600 MB), assigns cells to mice from hashtags, defines clones, builds the embedding, runs Threadfin and explains every output, including how to tell biology from a sequencing-library effect. It was executed end to end in a fresh environment before release and takes about 6 minutes on 8 CPU cores (about 8 GB of memory).

A clone embedding that holds together

UMAP draws continuous populations of clones as one picture only for suitable settings, and the suitable settings differ between datasets. Threadfin therefore chooses them for each dataset by default: it lays the clones out over a ladder of settings, scores each layout for coherence (the fraction of clones in one connected piece) and for fidelity to the clone profiles, and keeps the most faithful setting that holds together in every layout it tried. A map is not allowed to be more fragmented than the clone profiles themselves, and the final map is checked once more before it is returned. The record of the choice is stored with the results, and the settings can always be fixed by hand (Tutorial 3).

tf.tl.embed_clones(adata)                               # automatic (default)
tf.tl.embed_clones(adata, n_neighbors=30, min_dist=0.3) # by hand
tf.tl.map_quality(features, xy)                         # score any clone map (profiles, coordinates)

How it works

1. Clones are defined from paired heavy- and light-chain sequences within each donor (Dandelion).
2. Clone profiles. Each cell is described relative to the mean of its sampling context. The cells of a clone are summarised by a kernel mean embedding of their state distribution, so a clone divided between two states differs from a clone in an intermediate state, and the profile is shrunk towards the context according to the number of cells (best linear unbiased prediction under a random-effects model).
3. Clonal coherence is the intraclass correlation of state within clones, tested against clone labels permuted within samples.
4. The clone embedding is a UMAP of the clone profiles with settings chosen as above.
5. Clonal programmes are graph communities of clones; each split is tested against a single Gaussian group, and each programme's stability is estimated by bootstrap resampling.
6. Clone-level tests quantify how much of the variance between clone profiles a label explains, with labels permuted among the clones of each donor.

The full description, with references, is in Methods.

Documentation

Citation

A manuscript describing Threadfin is in preparation. Until it is available, please cite this repository (CITATION.cff) and the tools Threadfin builds on: Dandelion (Suo et al., Nat. Biotechnol. 2024) for clone definition, Scanpy (Wolf et al., Genome Biol. 2018) and UMAP (McInnes et al., J. Open Source Softw. 2018).

How Threadfin compares with related tools

Clonotrace, clone2vec, Benisse and CoNGA also combine lineage and transcriptome, and answer related but different questions. On the same data and clones (benchmark, with a one-page overview): Threadfin and Benisse represent every clone with two or more cells by default (clone2vec starts at three cells, Clonotrace at ten), and Threadfin's test of clonal coherence keeps its error rate when samples differ, where per-clone graph scores and cell-level tests do not. For recovering measured clone annotations Threadfin is comparable to clone2vec and to a simple mean of the cells' coordinates, and better than Clonotrace at its default and than Benisse; in simulation, clone2vec groups clones by their planted programme and tells mixed from in-between clones more accurately. Threadfin needs a fraction of the others' time (its clone profiles take seconds; clone2vec about 30 times longer, Clonotrace 300 to 2,400 times) and finished every run without help; a million cells take about an hour on 8 cores (scalability). The benchmark reports every result, including those where Threadfin is not the best.

Related tools

• Dandelion — single-cell immune receptor analysis; Threadfin's clone definition.
• Clonotrace — distances between expanded clones on the cell-state graph after label spreading.
• clone2vec — clone embeddings learned from which clones' cells are neighbours.
• Benisse — a joint space of BCR sequences and expression.
• CoNGA — graph-vs-graph analysis of T-cell receptor and expression similarity.
• Scirpy — single-cell immune receptor analysis in the scverse ecosystem; Threadfin reads its AnnData and MuData objects.
• Scanpy and AnnData — the data structures and single-cell tools Threadfin works with.
• Immcantation — B-cell receptor repertoire analysis, including clonal lineage reconstruction.

Support

Please report bugs and ask questions through GitHub issues.

License

MIT; see LICENSE.