Add dataset card (README)

README.md

@@ -1,3 +1,135 @@
 ---
 license: mit
+pretty_name: BenchRep-T
+size_categories:
+  - 1K<n<10K
+tags:
+  - immunology
+  - t-cell-receptor
+  - tcr-beta
+  - immune-repertoire
+  - AIRR
+  - immunoseq
+  - repertoire-classification
 ---
+
+# 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 four 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
+```
+
+## 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 |
+
+## Files
+
+- **`repertoires/*.tsv.gz`** — one gzip-compressed, tab-separated file per specimen; each row is a TCRβ
+  clone/rearrangement. 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`.
+
+## 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`).
+
+## 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
+```
+
+## 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.
+
+## Citation
+
+This dataset accompanies a manuscript currently under **double-blind peer review**.
+Author and citation details will be added once the review process is complete.