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hlt013-sample

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Tabular Classification
Tabular Regression
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Synthetic
healthcare
genomics
variant-calling
vcf
vep
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - healthcare
  - genomics
  - variant-calling
  - vcf
  - vep
  - cadd
  - clinvar
  - gnomad
  - rna-seq
  - bulk-rna-seq
  - single-cell
  - scrna-seq
  - pbmc
  - gene-expression
  - polygenic-risk-score
  - prs
  - pharmacogenomics
  - pgx
  - cpic
  - pharmgkb
  - cyp2d6
  - cyp2c19
  - ancestry
  - grch38
  - hg38
  - titv
  - hardy-weinberg
  - tabula-sapiens
  - gtex
  - 10x-genomics
pretty_name: HLT-013 Synthetic Multi-Modal Genomics Dataset (Sample Preview)
size_categories:
  - 1K<n<10K

HLT-013 — Synthetic Multi-Modal Genomics Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-013 commercial product from XpertSystems.ai.

A fully synthetic multi-modal genomics dataset combining variant calls (VCF-style with VEP/CADD/ClinVar/gnomAD annotations), bulk RNA-seq gene expression (5 tissues × 2,000 genes), single-cell RNA-seq PBMC profiles (10 cell types), polygenic risk scores (50 traits across 5 disease domains), and pharmacogenomics star allele calls (25 CPIC-actionable genes) — all linked through 1,000 individuals across 5 ancestry superpopulations (EUR/AFR/EAS/AMR/SAS, gnomAD-calibrated).

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real patient data, no PHI, no real genome sequences, no real variant calls. Population-level distributions match published gnomAD / ClinVar / VEP / CPIC / Tabula Sapiens benchmarks but the genomic profiles are computationally generated.

What's in this sample

1,000 individuals × 6 multi-modal genomic data tables linked by sample_id.

File Rows × Cols Description
cohort_manifest.csv 1,000 × 10 Individual master — ancestry, sex, age, sequencing type, mean coverage, pct bases ≥20x, consent tier
variants_annotated.csv 600 × 14 VCF-style: CHROM/POS/RSID/REF/ALT/GT/GQ/DP + VEP consequence + CADD Phred + ClinVar sig + gnomAD AF + HWE p-value
gene_expression.csv 2,000 × 7 Bulk RNA-seq gene panel — mean log2TPM, SD, CV, % expressed, housekeeping flag
scrna_pbmc.csv ~1,900 × 11 Single-cell PBMC: cell type, cluster ID, UMAP coords, n_genes, n_counts, pct_mito, doublet score
polygenic_risk_scores.csv 1,000 × 152 50 PRS traits × (raw score + ancestry-adjusted percentile + risk tier) per individual
pharmacogenomics.csv 1,000 × 102 25 PGx genes × (star allele class + CPIC recommendation + ACMG actionable + drug-specific guidance)
metadata.json — Run manifest: seed, genome build, ancestry distribution, Ti/Tv, tissue/cell-type/PRS/PGx counts

Total: ~2.4 MB across 8 files.

Schema highlights

cohort_manifest.csv (10 columns)

sample_id, ancestry_superpop (EUR/AFR/EAS/AMR/SAS), sex, age, cohort_id, genome_build (GRCh38), sequencing_type (WGS/WES), mean_coverage (numeric, 30x WGS / 100x WES mix), pct_bases_20x (QC metric), consent_tier (research/clinical/broad)

variants_annotated.csv (14 columns)

Position: CHROM (1-22, X), POS (genomic coordinate), RSID (rs identifier), REF, ALT, variant_type (SNP/InDel)

Genotype: GT (0/0, 0/1, 1/1), GQ (genotype quality 0-99), DP (read depth)

Annotation: consequence (VEP terms: intergenic / intron / synonymous / missense / 3'UTR / 5'UTR / splice_region / stop_gained / splice_donor / splice_acceptor / frameshift / inframe_indel), CADD_phred (deleteriousness 0-50+), ClinVar_sig (Benign / Likely_benign / VUS / Likely_pathogenic / Pathogenic), AF_gnomAD (allele frequency 0-1)

Population genetics: HWE_pval (Hardy-Weinberg Equilibrium p-value)

gene_expression.csv (7 columns)

gene_id (ENSG-style), gene_name, mean_log2TPM, sd_log2TPM, cv (coefficient of variation), pct_expressed (% of individuals with detectable expression), is_housekeeping (flag for stably-expressed reference genes)

scrna_pbmc.csv (11 columns)

sample_id, cell_barcode, cell_type (10 types: CD4_T_naive, CD4_T_memory, CD8_T_cytotoxic, B_cell_naive, B_cell_memory, NK_cell, Monocyte_classical, Monocyte_nonclassical, pDC, Platelet), cluster_id, UMAP_1, UMAP_2, n_genes, n_counts, pct_mito, doublet_score, cell_type_confidence

polygenic_risk_scores.csv (152 columns)

sample_id, ancestry, plus 50 traits × 3 fields each:

  • PRS_<trait> — raw polygenic score
  • PRS_<trait>_pct — ancestry-adjusted percentile (0-100)
  • PRS_<trait>_tier — risk tier (Low/Intermediate/High)

50 traits across 5 domains:

  • CVD (10): coronary_artery_disease, atrial_fibrillation, heart_failure, hypertension, stroke, peripheral_artery_disease, abdominal_aortic_aneurysm, hypertrophic_cardiomyopathy, dilated_cardiomyopathy, long_qt_syndrome
  • Metabolic (10): type2_diabetes, BMI, LDL_cholesterol, HDL_cholesterol, triglycerides, fasting_glucose, HbA1c, type1_diabetes, obesity, NAFLD
  • Oncology (10): breast_cancer, prostate_cancer, colorectal_cancer, lung_cancer, melanoma, ovarian_cancer, pancreatic_cancer, glioma, leukemia, lymphoma
  • Autoimmune (10): rheumatoid_arthritis, type1_diabetes_autoimmune, MS, lupus, psoriasis, IBD_crohns, IBD_ulcerative_colitis, celiac, asthma, atopic_dermatitis
  • Psychiatric (10): depression, bipolar, schizophrenia, anxiety, ADHD, autism, alzheimers, parkinsons, alcohol_dependence, smoking_behavior

pharmacogenomics.csv (102 columns)

sample_id, ancestry, plus 25 genes × 4 fields each:

  • PGx_<gene>_class — predicted phenotype (NM=Normal Metabolizer / IM=Intermediate / PM=Poor / RM=Rapid / UM=Ultrarapid)
  • PGx_<gene>_CPIC — CPIC dosing recommendation (Standard/Reduce/Increase/Avoid)
  • PGx_<gene>_ACMG_actionable — ACMG SF v3.2 actionable flag
  • PGx_<gene>_recommendation — drug-specific guidance text

25 genes (CPIC Level A or B): CYP2D6, CYP2C19, CYP2C9, CYP3A5, CYP2B6, CYP1A2, TPMT, NUDT15, DPYD, SLCO1B1, VKORC1, UGT1A1, HLA-B, HLA-A, CYP4F2, IFNL3, G6PD, RYR1, CACNA1S, ABCG2, F5, MTHFR, NAT2, ATM, BRCA1

Calibration source story

The full HLT-013 generator anchors all distributions to authoritative genomics references:

  • gnomAD v4 (Karczewski et al. 2020) — Population allele frequencies, ancestry superpopulation proportions
  • VEP (McLaren et al. 2016) — Variant Effect Predictor consequence annotation
  • ClinVar (Landrum et al. 2018) — Clinical variant significance database
  • CADD (Rentzsch et al. 2019) — Combined Annotation Dependent Depletion scores
  • CPIC — Clinical Pharmacogenetics Implementation Consortium dosing guidelines
  • PharmGKB — Gene-drug interaction knowledge base
  • 10x Genomics PBMC 10k reference — Single-cell PBMC cell type proportions
  • Tabula Sapiens (Quake Lab) — Cross-tissue cell type catalog
  • GTEx Consortium — Tissue-specific gene expression
  • PGS Catalog (Lambert et al. 2021) — Polygenic score trait coverage
  • ACMG SF v3.2 — Secondary findings actionable variant list

Sample-scale validation scorecard

Metric Observed Target Status Source
EUR ancestry share 40.2% 40% ± 5% ✅ PASS gnomAD v4
Ancestry superpop count 5 5 ✅ PASS gnomAD
Ti/Tv ratio 2.31 2.06 ± 0.80 ✅ PASS Wang et al. (2015)
ClinVar P/LP rate 2.3% 2.5% ± 2.5% ✅ PASS ClinVar
Mean coverage 42.1x 42 ± 10 ✅ PASS Clinical genomics QC
Cell type diversity 10 10 ✅ PASS 10x Genomics PBMC 10k
PRS trait count 50 50 ✅ PASS PGS Catalog
PGx gene count 25 25 ✅ PASS CPIC Level A/B
CYP2D6 NM rate 66.7% 65% ± 15% ✅ PASS CPIC + PharmGKB
Expression tissue count 5 5 ✅ PASS Tabula Sapiens / GTEx

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

Loading examples

Pandas — explore the cohort 

import pandas as pd

cohort = pd.read_csv("cohort_manifest.csv")
variants = pd.read_csv("variants_annotated.csv")
prs = pd.read_csv("polygenic_risk_scores.csv")
pgx = pd.read_csv("pharmacogenomics.csv")

# Ancestry distribution
print(cohort["ancestry_superpop"].value_counts(normalize=True).round(3))

# Variant consequence breakdown
print(variants["consequence"].value_counts(normalize=True).round(3))

# ClinVar significance
print(variants["ClinVar_sig"].value_counts())

Variant filtering 

import pandas as pd

variants = pd.read_csv("variants_annotated.csv")

# High-impact variants (Pathogenic + CADD > 25)
high_impact = variants[
    (variants["ClinVar_sig"].isin(["Pathogenic", "Likely_pathogenic"])) |
    (variants["CADD_phred"] > 25)
]
print(f"High-impact variants: {len(high_impact)}")

# Rare variants (gnomAD AF < 1%)
rare = variants[variants["AF_gnomAD"] < 0.01]
print(f"Rare variants: {len(rare)}")

# HWE-departure variants
hwe_violations = variants[variants["HWE_pval"] < 0.001]
print(f"HWE violations: {len(hwe_violations)}")

PRS risk stratification 

import pandas as pd

prs = pd.read_csv("polygenic_risk_scores.csv")

# Top 10% CAD risk individuals
high_cad = prs[prs["PRS_coronary_artery_disease_pct"] >= 90]
print(f"High CAD risk: {len(high_cad)} individuals")

# Multi-trait risk profile
risk_traits = ["coronary_artery_disease", "type2_diabetes", "breast_cancer"]
for trait in risk_traits:
    tier_col = f"PRS_{trait}_tier"
    if tier_col in prs.columns:
        print(f"\n{trait} risk tier distribution:")
        print(prs[tier_col].value_counts(normalize=True).round(3))

PGx phenotype distribution 

import pandas as pd

pgx = pd.read_csv("pharmacogenomics.csv")

# CYP2D6 phenotype by ancestry
print(pd.crosstab(pgx["ancestry"], pgx["PGx_CYP2D6_class"], normalize="index").round(3))

# Actionable findings (ACMG SF v3.2)
actionable_cols = [c for c in pgx.columns if c.endswith("_ACMG_actionable")]
n_actionable = pgx[actionable_cols].sum(axis=1)
print(f"\nIndividuals with ≥1 actionable PGx finding:")
print(f"  None:    {(n_actionable == 0).sum()}")
print(f"  1 gene:  {(n_actionable == 1).sum()}")
print(f"  2+ genes: {(n_actionable >= 2).sum()}")

scRNA-seq cell type analysis 

import pandas as pd

scrna = pd.read_csv("scrna_pbmc.csv")

# Cell type proportions per sample
cell_pcts = scrna.groupby("sample_id")["cell_type"].value_counts(normalize=True).unstack(fill_value=0)
print("Mean cell type proportions:")
print(cell_pcts.mean().sort_values(ascending=False).round(3))

# QC metrics by cell type
print("\nQC metrics by cell type:")
print(scrna.groupby("cell_type")[["n_genes", "pct_mito", "doublet_score"]].mean().round(2))

Hugging Face Datasets 

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt013-sample", data_files={
    "cohort":     "cohort_manifest.csv",
    "variants":   "variants_annotated.csv",
    "expression": "gene_expression.csv",
    "scrna":      "scrna_pbmc.csv",
    "prs":        "polygenic_risk_scores.csv",
    "pgx":        "pharmacogenomics.csv",
})
print(ds)

Suggested use cases

  • Variant prioritization ML — train classifiers on CADD + ClinVar + AF features to predict pathogenicity
  • PRS-disease prediction modeling — multi-trait ML for absolute risk stratification
  • Ancestry imputation — train ancestry callers from variant features
  • Variant Effect Predictor pipeline testing — schema-compliant data for VEP/SnpEff annotation pipeline development
  • Pharmacogenomic CDS rules engine testing — populate PGx clinical decision support systems
  • scRNA-seq cell type classification — train cell type callers from gene expression + UMAP coordinates
  • HWE violation detection — flag spurious genotype calls or population structure
  • Multi-modal genomics integration — joint modeling across variants + expression + PRS + PGx
  • Clinical genomics LIMS testing — populate clinical genomics pipelines with realistic synthetic patients
  • Healthcare AI pretraining — pretrain models on synthetic genomic profiles before fine-tuning on real biobank data
  • Educational use — graduate genomics, biostatistics, and precision medicine coursework

Sample vs. full product

Aspect This sample Full HLT-013 product
Individuals 1,000 100,000+ (default) up to 1M
Variants per individual 600 representative Full WGS ~6.5M variants
Genes (bulk expression) 2,000 Full transcriptome ~20,000 genes
scRNA cells per sample ~2 (sampled) ~200 cells per sample
PRS traits 50 50 (full coverage)
PGx genes 25 25 (full CPIC Level A/B coverage)
Schema identical identical
Calibration identical identical
License CC-BY-NC-4.0 Commercial license

The full product unlocks:

  • Up to 1M individuals for biobank-scale genomic ML training
  • Full WGS variant calls (~6.5M variants per individual)
  • Full transcriptome (20,000+ genes)
  • Dense scRNA-seq profiles (200+ cells per sample)
  • GWAS summary statistics for the 50 PRS traits
  • Family pedigrees for trio/quartet analysis
  • Commercial use rights

Contact us for the full product.

Limitations & honest disclosures

  • Sample is preview-only. 1,000 individuals × 600 representative variants is enough to demonstrate schema and calibration, but is not statistically sufficient for serious GWAS, PRS development, or rare variant analysis. Use the full product (100K+) for serious work.
  • Variant set is sub-sampled. Each individual carries 600 representative variants (mix of SNPs + InDels), not the ~6.5M variants from full WGS. Variant positions are real-coordinate-valid but sparse.
  • scRNA-seq cells per sample are sparse (~2 cells/sample at preview scale). Real PBMC scRNA-seq experiments yield 200-1000 cells per sample. The sample compresses this for size — full product has dense per-sample profiles.
  • Gene expression is panel-summary, not per-individual. The gene_expression.csv file gives population-level summary statistics (mean log2TPM, SD, CV) across the 1,000 individuals, NOT individual-specific TPM values. For per-individual expression matrices, use the full product.
  • Housekeeping gene flag rate runs slightly high (~7.5% vs typical 1-3%). The generator marks more genes as housekeeping than strict biological definitions. Cross-reference with HK genes lists (Eisenberg & Levanon 2013) if exact housekeeping calls matter.
  • Ti/Tv ratio variance is high at 600-variant sample scale (1.78-2.68 across seeds vs target 2.06). This is small-sample noise — full WGS at 6.5M variants converges tightly to the gnomAD target.
  • RSIDs are synthetic. Generated RSIDs follow the rsXXXXXXX format but do NOT correspond to real dbSNP entries.
  • gnomAD AF values are sampled from realistic distributions but are NOT real allele frequencies. Do not use this data for variant frequency reporting.
  • ClinVar IDs not included. Variants have ClinVar_sig classifications but no real ClinVar variation IDs.
  • PRS scores are simulated, not based on real GWAS effect sizes. Distributions match published PRS percentile shapes but specific scores do NOT reflect real allele effects.
  • PGx phenotype calls follow CPIC frequency distributions but are NOT mechanistic. Star allele class assignments are population-frequency-driven, not derived from underlying CYP/TPMT/etc. variant calls in variants_annotated.csv.
  • Synthetic, not derived from real biobank data. Distributions match published gnomAD/ClinVar/CPIC/Tabula Sapiens references but do NOT reflect any specific real cohort (UK Biobank, All of Us, etc.).

Ethical use guidance

This dataset is designed for:

  • Genomic ML methodology development
  • Clinical genomics pipeline testing
  • PRS modeling research
  • Pharmacogenomics CDS rule engine development
  • scRNA-seq cell type annotation methodology
  • Healthcare AI pretraining for genomic prediction tasks
  • Educational use in clinical genomics, precision medicine, and biostatistics

This dataset is not appropriate for:

  • Making clinical genetic diagnoses about real individuals
  • Real PRS reporting for real patients without validated ancestry-matched reference panels
  • Pharmacogenomic prescribing decisions for real patients without CPIC consultation
  • Variant pathogenicity calls without ACMG framework validation on real ClinVar data
  • Ancestry-based discriminatory modeling
  • Population-genetic claims about real ethnic groups

Companion datasets in the Healthcare vertical

  • HLT-001 — Synthetic Patient Population (CDC/NHANES)
  • HLT-002 — Synthetic EHR (FHIR R4)
  • HLT-003 — Synthetic Clinical Trial
  • HLT-004 — Synthetic Disease Progression
  • HLT-005 — Synthetic Hospital Admission
  • HLT-006 — Synthetic Medical Imaging
  • HLT-007 — Synthetic Drug Response
  • HLT-008 — Synthetic Healthcare Claims
  • HLT-009 — Synthetic ICU Vital Sign Monitoring
  • HLT-010 — Synthetic Hospital Resource Usage
  • HLT-011 — Synthetic Rare Disease + Trial Eligibility
  • HLT-012 — Synthetic Pandemic Spread
  • HLT-013 — Synthetic Multi-Modal Genomics (you are here)

Use HLT-001 through HLT-013 together for the full healthcare data stack — and HLT-013 specifically extends the catalog into precision medicine & clinical genomics, complementing HLT-007 (drug response with PGx hooks) and HLT-011 (rare disease with gene-variant calls).

Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt013_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-013 Synthetic Multi-Modal Genomics Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt013-sample}
}

Contact

Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.