| --- |
| license: mit |
| pretty_name: BenchRep-T |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - immunology |
| - t-cell-receptor |
| - tcr-beta |
| - immune-repertoire |
| - AIRR |
| - immunoseq |
| - repertoire-classification |
| configs: |
| - config_name: Mal-ID |
| data_files: |
| - split: specimens |
| path: immuneML/Mal-ID/metadata.csv |
| - config_name: Savola_RA |
| data_files: |
| - split: specimens |
| path: immuneML/immunoSEQ/Savola_RA/metadata.csv |
| - config_name: Musvosvi_TB |
| data_files: |
| - split: specimens |
| path: immuneML/immunoSEQ/Musvosvi_TB/metadata.csv |
| - config_name: Rawat_T1D |
| data_files: |
| - split: specimens |
| path: immuneML/immunoSEQ/Rawat_T1D/metadata.csv |
| - config_name: Mitchell_T1D |
| data_files: |
| - split: specimens |
| path: immuneML/immunoSEQ/Mitchell_T1D/metadata.csv |
| - config_name: Emerson_CMV |
| data_files: |
| - split: specimens |
| path: immuneML/immunoSEQ/Emerson_CMV/metadata.csv |
| --- |
| |
| # BenchRep-T |
|
|
| **BenchRep-T** is a benchmark for **TCRβ (T-cell receptor β-chain) immune-repertoire classification**. |
| It bundles the multi-disease **Mal-ID** cohort together with five external disease cohorts |
| (sequenced on the Adaptive Biotechnologies **immunoSEQ** platform), providing per-specimen |
| repertoire files, harmonized sample metadata, and auxiliary files used for the |
| sequencing-depth *scaling-law* and *driver-sequence* experiments. |
|
|
| Each **specimen** (one sequenced repertoire) is one example; the classification target is the |
| specimen-level `disease` label. |
|
|
| ## Dataset structure |
|
|
| ``` |
| BenchRep-T/ |
| ├── Mal-ID/ |
| │ ├── metadata.tsv # 550 specimen annotations + labels |
| │ ├── repertoires/ # 550 per-specimen TCRβ rearrangement tables (.tsv.gz) |
| │ ├── scaling_exp_depth_indices_max75k.json.gz # sampling indices for the depth scaling-law experiment |
| │ └── vdjdb_minervina_driver_seq_matches.csv # repertoire ↔ public/driver-clone matches (driver-seq experiment) |
| ├── immunoSEQ/ |
| │ ├── Savola_RA/ # Rheumatoid Arthritis |
| │ │ ├── metadata.tsv |
| │ │ └── repertoires/ # 91 .tsv.gz |
| │ ├── Musvosvi_TB/ # Tuberculosis (progression) |
| │ │ ├── metadata.tsv |
| │ │ └── repertoires/ # 140 .tsv.gz |
| │ ├── Rawat_T1D/ # Type 1 Diabetes |
| │ │ ├── metadata.tsv |
| │ │ └── repertoires/ # 614 .tsv.gz |
| │ ├── Mitchell_T1D/ # Type 1 Diabetes |
| │ │ ├── metadata.tsv |
| │ │ └── repertoires/ # 196 .tsv.gz |
| │ └── Emerson_CMV/ # Cytomegalovirus exposure |
| │ ├── metadata.tsv |
| │ └── repertoires/ # 761 .tsv.gz |
| └── immuneML/ # immuneML-ready AIRR export of every cohort (see immuneML/README.md) |
| ├── Mal-ID/, immunoSEQ/<cohort>/ # metadata.csv + AIRR repertoires/*.tsv.gz |
| ├── tasks/<task>/ # task metadata + fixed fold{0,1,2}_{train,test}.csv |
| ├── specs/<task>.yaml # runnable immuneML specifications |
| └── README.md, MANIFEST.tsv, export_report.json |
| ``` |
|
|
| ## Cohorts |
|
|
| | Group | Path | Classification task | Repertoires | Label composition | Repertoire format | Source study | |
| |---|---|---|---|---|---|---| |
| | Mal-ID | `Mal-ID/` | Multi-disease vs. Healthy/Background | 550 | Healthy/Background 197, HIV 98, Lupus 64, Covid19 58, Influenza 37, T1D 96 | AIRR-style rearrangement table | Zaslavsky et al. 2025 | |
| | Savola_RA | `immunoSEQ/Savola_RA/` | Rheumatoid Arthritis vs. Healthy | 91 | RA 71, Healthy 20 | immunoSEQ export | Savola et al. 2017 | |
| | Musvosvi_TB | `immunoSEQ/Musvosvi_TB/` | TB Progressor vs. Controller | 140 | Progressor 63, Controller 77 | immunoSEQ export | Musvosvi et al. 2023 | |
| | Rawat_T1D | `immunoSEQ/Rawat_T1D/` | Type 1 Diabetes vs. Control | 614 | T1D 426, Healthy/Background 188 | immunoSEQ export (reduced) | Rawat et al. 2026 | |
| | Mitchell_T1D | `immunoSEQ/Mitchell_T1D/` | Type 1 Diabetes vs. Healthy | 196 | T1D 171, Healthy/Background 25 | immunoSEQ export | Mitchell et al. 2022 | |
| | Emerson_CMV | `immunoSEQ/Emerson_CMV/` | CMV+ vs. Healthy/Background | 761 | CMV 340, Healthy/Background 421 | immunoSEQ export | Emerson et al. 2017 | |
|
|
| ## Files |
|
|
| - **`repertoires/*.tsv.gz`** — one gzip-compressed, tab-separated file per specimen; each row is one TCRβ |
| sequencing read (Mal-ID; identical reads are not collapsed) or one unique rearrangement with its count (immunoSEQ cohorts). The file stem is the specimen identifier (for the immunoSEQ cohorts it matches the |
| `specimen_label`/`sample_name`/`filename` column in that cohort's `metadata.tsv`; Rawat_T1D file names carry a `_TCRB` suffix). |
| - **`metadata.tsv`** — one row per specimen with the classification label and per-cohort annotations |
| (schemas differ across cohorts — see below). |
| - **`Mal-ID/scaling_exp_depth_indices_max75k.json.gz`** — precomputed read-sampling indices (up to 75k reads/specimen) |
| defining the subsampled repertoires used in the sequencing-depth scaling-law experiment. |
| - **`Mal-ID/vdjdb_minervina_driver_seq_matches.csv`** — matches between repertoire clones and known |
| public / antigen-specific "driver" TCRs (VDJdb + Minervina et al.), used for the driver-sequence experiment. |
| Columns: `disease, sample_cdr3, sample_vgene, sample_jgene, public_clone_cdr3, public_clone_vgene, public_clone_jgene, similarity, score, filename`. |
|
|
| ## Repertoire schemas |
|
|
| **Mal-ID** (AIRR-style, ~120 columns) — key fields: `sequence_id, repertoire_id, locus, v_call, d_call, j_call, |
| cdr3, cdr3_aa, junction, junction_aa, productive, participant_label, specimen_time_point`, plus full |
| nucleotide alignment / insertion-deletion columns. |
|
|
| **immunoSEQ cohorts** (Savola_RA, Musvosvi_TB, Mitchell_T1D; Adaptive immunoSEQ export, ~54 columns) — key fields: |
| `nucleotide, cdr3_aa, count (templates/reads), frequencyCount (%), v_call, j_call, vGeneName, jGeneName, |
| sequenceStatus, estimatedNumberGenomes, sequence, num_reads, repertoire_id, participant_label`. |
| |
| **Rawat_T1D** (reduced immunoSEQ-derived, 17 columns): `cdr3_aa, count (templates/reads), frequency, nucleotide, |
| v_call, d_gene, j_call, sequenceStatus, v_family, d_family, j_family, v_resolved, d_resolved, j_resolved, |
| sequence, num_reads`. |
|
|
| **Emerson_CMV** (minimal, 7 columns): `cdr3_aa, v_call, j_call, sequence, num_reads, repertoire_id, participant_label`. |
| |
| ## Metadata schemas |
| |
| All `metadata.tsv` files share `participant_label`, `specimen_label`, and `disease` (the classification target). |
| Cross-validation fold assignments are provided per cohort (`CV_fold` / `fold` / |
| `malid_cross_validation_fold_id_when_in_test_set`). Additional per-cohort columns: |
| |
| - **Mal-ID / Mitchell_T1D**: `specimen_time_point, study_name, available_gene_loci, disease_subtype, age, sex, ancestry`. |
| - **Savola_RA / Musvosvi_TB**: immunoSEQ sample statistics (`total_templates, productive_templates, |
| fraction_productive, productive_simpson_clonality, sample_tags, sku, test_name`, …). |
| - **Rawat_T1D**: subject clinical/HLA fields (`diabetes_status, sex, age, duration, HLA A/B/C/DPB1/DQB1/DRB1…, |
| hla_high_risk_type, autoantibody statuses, ML_class`). |
| - **Emerson_CMV**: same base fields as Mal-ID/Mitchell_T1D (`specimen_time_point, study_name, available_gene_loci, |
| disease_subtype, age, sex, ancestry`) plus `repertoire_file, emerson_subject_id, cohort, cohort_name, race, |
| ethnicity, race_and_ethnicity, known_cmv_status, metadata_source`. |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import pandas as pd, glob, os |
| |
| # Download one cohort (metadata + repertoires) |
| local = snapshot_download( |
| repo_id="lordim/BenchRep-T", |
| repo_type="dataset", |
| allow_patterns="immunoSEQ/Savola_RA/*", |
| ) |
| |
| meta = pd.read_csv(f"{local}/immunoSEQ/Savola_RA/metadata.tsv", sep="\t") |
| rep_files = glob.glob(f"{local}/immunoSEQ/Savola_RA/repertoires/*.tsv.gz") |
| rep = pd.read_csv(rep_files[0], sep="\t") # pandas reads .gz transparently |
| ``` |
|
|
| ## immuneML-ready AIRR export |
|
|
| `immuneML/` holds every cohort above in one AIRR Rearrangement schema that [immuneML](https://github.com/uio-bmi/immuneML) can import directly: |
|
|
| - **Repertoires:** the same specimens and rows as the native files, in 20 AIRR columns that include all 14 required fields. `cdr3_aa` is unchanged. `junction`/`junction_aa` are rebuilt from the nucleotide data where they translate back to `cdr3_aa`: 99.78% of Mal-ID rows, and more than 99.99% in every other cohort. |
| - **Metadata:** one comma-separated `metadata.csv` per cohort, with `subject_id` set to the specimen and BenchRep-T's fixed `fold`. |
| - **Tasks and specs:** for each of the 11 benchmark tasks, the task's specimens, the three fixed train/test fold splits, and a runnable immuneML specification. |
|
|
| Tested with immuneML 3.0.27; install it with `pandas<3`. [`immuneML/README.md`](immuneML/README.md) documents every conversion rule and caveat. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="lordim/BenchRep-T", repo_type="dataset", local_dir="BenchRep-T", |
| allow_patterns=["immuneML/immunoSEQ/Savola_RA/*", "immuneML/tasks/ra/*", |
| "immuneML/specs/ra.yaml"], |
| ) |
| # then, inside BenchRep-T/: immune-ml immuneML/specs/ra.yaml results/ra |
| ``` |
|
|
| ## License |
|
|
| Released under the **MIT License**. |
|
|
| ## References |
|
|
| Source studies for each cohort: |
|
|
| - **Mal-ID** — Zaslavsky, Maxim E., et al. "Disease diagnostics using machine learning of B cell and T cell receptor sequences." *Science* 387.6736 (2025): eadp2407. |
| - **Savola_RA** — Savola, Paula, et al. "Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis." *Nature Communications* 8.1 (2017): 15869. |
| - **Musvosvi_TB** — Musvosvi, Munyaradzi, et al. "T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection." *Nature Medicine* 29.1 (2023): 258-269. |
| - **Rawat_T1D** — Rawat, Puneet, et al. "Identification of a type 1 diabetes–associated T cell receptor repertoire signature from the human peripheral blood." *Science Advances* 12.7 (2026): eadx7448. |
| - **Mitchell_T1D** — Mitchell, Angela M., et al. "Temporal development of T cell receptor repertoires during childhood in health and disease." *JCI Insight* 7.18 (2022): e161885. |
| - **Emerson_CMV** — Emerson, Ryan O., et al. "Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire." *Nature Genetics* 49.5 (2017): 659-665. |
| |
| ## Citation |
| |
| BenchRep-T is described in the following paper, to appear in the **NeurIPS 2026 Evaluations and Datasets Track**: |
| |
| > Alejandro Buendia\*, Chiho Im\*, Anshul Kundaje, Scott D Boyd, and Liel Cohen-Lavi. "BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics." *Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track*, 2026. |
| > |
| > \* Equal contribution (co-first authors). |
| |
| ```bibtex |
| @inproceedings{buendia2026benchrep, |
| title = {BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics}, |
| author = {Buendia, Alejandro and Im, Chiho and Kundaje, Anshul and Boyd, Scott D and Cohen-Lavi, Liel}, |
| booktitle = {Advances in Neural Information Processing Systems, Evaluations and Datasets Track}, |
| year = {2026} |
| } |
| ``` |
| |
| Code: [github.com/boyd-lab/BenchRep-T](https://github.com/boyd-lab/BenchRep-T). A preprint is available on [bioRxiv](https://www.biorxiv.org/content/10.64898/2026.06.09.727013v1.abstract). |
| |
