TCR / BCR repertoire
Repertoire assembly and annotation with clonality, diversity, and clonal-expansion tracking from bulk or single-cell VDJ data.
Research Area
Immunology & Immuno-Oncology
The immune system is a moving target—millions of clones, dozens of cell states, and context that shifts with every perturbation. We bring the computational tools to resolve it: immune repertoires, single-cell states, neoantigens, and the tumor immune microenvironment, for research.
TCR / BCR repertoire
Single-cell & CITE-seq
HLA & neoantigens
Research-use · not clinical
Illustrative sample output
T cells
B cells
myeloid / NK
Overview
Resolving the immune system, cell by cell and clone by clone
Immune data is uniquely high-dimensional. A single sample holds millions of distinct T- and B-cell clones, dozens of interconverting cell states, and immune signals that only make sense in context—which tissue, which perturbation, which point in a response. Standard bulk pipelines flatten exactly the heterogeneity that matters, and getting real biology out needs methods built for immune repertoires, single-cell states, and antigen recognition.
We bring that toolkit to your immunology and immuno-oncology data. From TCR/BCR repertoire assembly and single-cell immune profiling to HLA typing, neoantigen prediction, immune deconvolution, and tumor-immune-microenvironment characterisation, we apply established, peer-reviewed methods and return results documented for reproducibility. Immunotherapy-response work is framed as research—correlates and signatures for cohort studies, not a clinical test.
Applications
What we analyse in immunology
The computational analyses that resolve immune biology—from repertoires and single-cell states to neoantigens and the tumor immune microenvironment.
Single-cell immune profiling
Immune-cell clustering, annotation, and state analysis from scRNA-seq and CITE-seq—with paired repertoire where available.
HLA typing & neoantigens
HLA typing from sequencing, peptide-MHC binding and neoantigen prediction, and immunogenicity ranking for vaccine research.
Immune deconvolution
Estimating immune-cell composition from bulk RNA to quantify infiltration across large cohorts without single-cell data.
Tumor immune microenvironment
Immune-infiltration scoring, immune states, and hot-versus-cold characterisation of the tumor microenvironment.
Cytometry analysis
High-dimensional flow and mass cytometry (CyTOF) analysis—clustering, dimensionality reduction, and differential abundance.
Spatial immunology
Spatial-transcriptomics analysis of immune context—where immune cells sit, and which neighbourhoods they form.
Immune signatures & response
Research immune signatures and correlates of immunotherapy response for cohort studies—never a clinical prediction.
The Pipeline
A representative immune-profiling workflow
A transparent, immune-aware sequence from raw data to resolved immune biology—each step suited to the data type and documented for reproducibility.
Adapted to your study: bulk vs. single-cell, RNA vs. repertoire vs. cytometry, tissue vs. blood. We confirm the plan with you before any compute begins.
Intake & QC
Raw reads, count matrices, or cytometry files are quality-checked—depth, doublets, ambient signal, and batch structure.
Tools: FastQC · MultiQC · QC metrics
Processing
Reads are aligned or cells called; repertoires assembled from VDJ data; cytometry data transformed and normalised.
Tools: Cell Ranger · MiXCR · CATALYST
Cell typing & annotation
Immune cells are clustered and annotated to reference cell types and states, with markers checked against known biology.
Tools: Seurat · Azimuth · celltypist
Repertoire & clonality
Clonality, diversity, and expansion are quantified, and repertoire is paired with single-cell phenotype where available.
Tools: scirpy · Immcantation · VDJtools
Deconvolution & TME
For bulk cohorts, immune composition is estimated and the tumor immune microenvironment characterised.
Tools: CIBERSORTx · quanTIseq · xCell
Signatures & correlates
Research immune signatures, HLA and neoantigen predictions, and response correlates are computed for the cohort.
Tools: ssGSEA · pVACtools · OptiType
Integration & reporting
Results become UMAPs, repertoire and composition figures, tables, and reproducible methods with every tool version.
Tools: ggplot2 · ComplexHeatmap · versioned manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Immune repertoire
MiXCR
Immcantation
TRUST4
IgBLAST
VDJtools
Single-cell immune
Seurat
Scanpy
scirpy
Azimuth
celltypist
HLA & neoantigen
OptiType
arcasHLA
pVACtools
NetMHCpan
MHCflurry
Deconvolution
CIBERSORTx
quanTIseq
MCP-counter
xCell
Cytometry
CATALYST
FlowSOM
diffcyt
Spectre
Spatial
Squidpy
Giotto
cell2location
SpatialDE
Signatures & response
GSVA / ssGSEA
immunedeconv
ESTIMATE
TIDE
Visualization & databases
ggplot2
ComplexHeatmap
IEDB
VDJdb
Key Databases
The immunology resources we draw on
Immune analysis is powered by curated epitope, receptor, and reference resources. We build on the community's authoritative, versioned data.
- IEDB
- Immune Epitope Database of experimentally characterised epitopes.
- IMGT
- The international ImMunoGeneTics reference for receptor genes.
- VDJdb
- Curated TCR sequences with known antigen specificity.
- McPAS-TCR
- Pathology-associated T-cell receptor sequences.
- IPD-IMGT/HLA
- The reference database of HLA alleles for typing.
- Human Cell Atlas
- Single-cell reference atlases for immune-cell annotation.
- MSigDB
- Curated immune and hallmark gene-set signatures.
- TCGA immune landscape
- Immune profiling across cancer types for context.
- Human Protein Atlas
- Immune-cell expression across genes and tissues.
Choosing an Approach
Bulk deconvolution vs. single-cell vs. cytometry
Different technologies resolve immunity at different scales. A quick orientation; we will help you match it to your question.
| Dimension | Bulk deconvolution | Single-cell | Cytometry |
|---|---|---|---|
| Resolution | Estimated proportions | Per-cell states & programs | Per-cell protein markers |
| Measures | Cell fractions from RNA | Transcriptome ± VDJ / protein | Curated marker panels |
| Scale & cost | Large cohorts, low cost | Fewer samples, higher cost | Many cells, moderate cost |
| Repertoire | Not resolved | Paired TCR/BCR possible | Not resolved |
| Best suited to | Cohort infiltration studies | Deep immune phenotyping | High-throughput cell counts |
What You Receive
A complete, documented deliverable
Not just a count matrix dropped in a folder—a resolved picture of the immune compartment, documented so it reproduces.
- Annotated immune-cell atlas with cluster markers & states
- TCR/BCR repertoire tables with clonality & diversity metrics
- HLA types & ranked neoantigen candidates, where applicable
- Immune-deconvolution & microenvironment scores across the cohort
- Research immune signatures & response correlates
- UMAPs, repertoire & composition figures (publication-quality)
- Reproducible methods with every tool & reference version
Built for reproducibility, not just a result
Immune analysis follows documented best practices—doublet and ambient-RNA control, batch handling, reference-based annotation checked against known markers, and repertoire metrics computed on properly filtered clones—so a cell state or an expanded clone is real biology, not a technical artifact. Immunotherapy-response work is framed honestly as research: we compute signatures and correlates for cohort studies, not a clinical prediction, and treatment decisions remain with clinicians.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and a result from today can be reproduced a year from now. We will also tell you honestly when a design or sample size won't support the conclusion you're after.
FAQ
Immunology & immuno-oncology questions
What immunology and immuno-oncology teams most often ask before starting.
Bulk and single-cell RNA-seq, TCR/BCR immune-repertoire sequencing, CITE-seq and multimodal single-cell, spatial transcriptomics, and flow or mass cytometry (FCS) data — starting from raw reads, count matrices, or cytometry files from your core.
Yes. We assemble and annotate TCR and BCR repertoires, quantify clonality and diversity, track clonal expansion across conditions or time, and pair repertoire with single-cell phenotype where you have paired data.
Yes. We type HLA from sequencing, predict peptide-MHC binding and neoantigens, and rank candidates by predicted immunogenicity — useful for vaccine and immunotherapy research.
Yes. We deconvolve immune-cell composition from bulk RNA, characterise immune states at single-cell resolution, and quantify infiltration and hot-versus-cold phenotypes to study the tumor immune microenvironment.
We compute research immune signatures and correlates associated with response (for example TMB, immune-infiltration and checkpoint signatures) for cohort studies. These are research analyses, not a clinical test — treatment decisions remain with clinicians.
Immuno-oncology is a research area we support through our core services — chiefly Transcriptomics & Expression, Multi-Omics Integration, and Biomarker & Variant Interpretation — applied to immune data. This page shows how they come together.
Related
Services & areas that power immunology work
Have immune data to resolve?
Tell us your organism, data type, and question—we'll scope it honestly, including if a different design would serve you better.