--- 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 ⚠️ **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_` — raw polygenic score - `PRS__pct` — ancestry-adjusted percentile (0-100) - `PRS__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__class` — predicted phenotype (NM=Normal Metabolizer / IM=Intermediate / PM=Poor / RM=Rapid / UM=Ultrarapid) - `PGx__CPIC` — CPIC dosing recommendation (Standard/Reduce/Increase/Avoid) - `PGx__ACMG_actionable` — ACMG SF v3.2 actionable flag - `PGx__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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (CDC/NHANES) - [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR (FHIR R4) - [HLT-003](https://huggingface.co/datasets/xpertsystems/hlt003-sample) — Synthetic Clinical Trial - [HLT-004](https://huggingface.co/datasets/xpertsystems/hlt004-sample) — Synthetic Disease Progression - [HLT-005](https://huggingface.co/datasets/xpertsystems/hlt005-sample) — Synthetic Hospital Admission - [HLT-006](https://huggingface.co/datasets/xpertsystems/hlt006-sample) — Synthetic Medical Imaging - [HLT-007](https://huggingface.co/datasets/xpertsystems/hlt007-sample) — Synthetic Drug Response - [HLT-008](https://huggingface.co/datasets/xpertsystems/hlt008-sample) — Synthetic Healthcare Claims - [HLT-009](https://huggingface.co/datasets/xpertsystems/hlt009-sample) — Synthetic ICU Vital Sign Monitoring - [HLT-010](https://huggingface.co/datasets/xpertsystems/hlt010-sample) — Synthetic Hospital Resource Usage - [HLT-011](https://huggingface.co/datasets/xpertsystems/hlt011-sample) — Synthetic Rare Disease + Trial Eligibility - [HLT-012](https://huggingface.co/datasets/xpertsystems/hlt012-sample) — 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: ```bibtex @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 - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) **Full product License:** Commercial — please contact for pricing.