zzhaobz/cembra-training-data

Cembra OA pseudo-SNP training data

Private, model-ready data for the Cembra pseudo-SNP SVD64 model. This package contains the exact reference/alternate sequence pairs consumed by the selected development model; it contains no individual-level genotype or phenotype records.

Configurations

supervised

• 168 OA variant examples: 56 positives and 112 matched controls.
• 56 complete matched triplets in 49 guarded independence components.
• Exact five-fold assignment: 39, 30, 45, 27, and 27 rows.
• Each example contains paired 3,000 bp hg38 REF/ALT sequences differing only at zero-based index 1,500.
• pip and related fields document cohort selection; they were not model features.
• data/supervised_full_metadata.tsv.gz retains all source/audit columns while removing model prediction columns.

pretraining

• 20,000 label-free pseudo-SNP sequence pairs sampled from hg38 autosomes.
• Seed 20260808; OA guarded intervals plus 3 kb were excluded.
• No labels, PIP, phenotype, source identity, GWAS p-values, or GWAS effect sizes were read during pretraining.
• Coordinates are retained in data/pseudo_snp_coordinates.tsv.

Minimal loading example

from datasets import load_dataset

supervised = load_dataset("zzhaobz/cembra-training-data", "supervised", split="train")
pretraining = load_dataset("zzhaobz/cembra-training-data", "pretraining", split="train")

For outer fold k, train on rows where outer_fold != k and validate on rows where outer_fold == k. Keep each matched_set_id intact.

Provenance

Supervised annotations derive from Supplementary Table 9 of Hatzikotoulas et al., Translational genomics of osteoarthritis in 1,962,069 individuals, DOI 10.1038/s41586-025-08771-z, distributed under CC BY 4.0. Sequence windows use the UCSC hg38 FASTA. Exact hashes, exclusions, and file inventories are recorded in DATASET_MANIFEST.json and provenance/.

The upstream 1.1 GB GWAS archive and full 3.2 GB FASTA are not duplicated: the exact model-ready 3 kb sequences are included here. No UK Biobank, arcOGEN, or other participant-level genotypes or phenotypes are present.

Intended use and limits

Research reproduction and method development only. Labels are fine-mapping proxy labels, not experimentally established causal truth. The dataset is development evidence, has no external confirmation claim, and is not suitable for clinical use.

See DATA_USE.md, LICENSE, and verify_dataset.py before reuse.

Verification

verify_dataset.py checks the package manifest, sequence invariants, labels, folds, and matched triplets. verify_reproduction.py independently reconstructs the governed cohort/fold map and recomputes the full 20,000 × 9,216 pseudo-SNP feature matrix. The recorded result is in VERIFICATION.md.