Datasets:

electricsheepafrica
/

africa-cancer-ethiopia

@@ -1,248 +1,143 @@
 ---
 language:
 - en
-tags:
-- dna-methylation
-- epigenetic-clock
-- promoter-methylation
-- global-methylation
-- breast-cancer
-- sub-saharan-africa
-license: cc-by-nc-4.0
-pretty_name: SSA Breast DNA Methylation Dataset (Women, Multi-ancestry)
 task_categories:
-- other
 size_categories:
-- 1K<n<10K
 ---
-# SSA Breast DNA Methylation Dataset (Women, Multi-ancestry, Synthetic)
-## Dataset summary
-This dataset provides a **synthetic DNA methylation cohort** conceptually linked to women with invasive breast cancer across multiple ancestry groups, with emphasis on **Sub-Saharan Africa (SSA)** and comparable reference populations.
-It captures **high-level methylation features** rather than site-level arrays, grounded in key literature on epigenetic clocks, promoter CpG methylation of tumour suppressor genes, global hypomethylation, and race/ancestry-associated methylation patterns:
-- **Epigenetic age and age acceleration** (Horvath/Hannum-like clocks).
-- **Global DNA methylation** (e.g., LINE-1 / LUMA-like indices).
-- **Promoter methylation** of canonical tumour suppressors (BRCA1, RASSF1A, CDH1, CDKN2A/p16, DAPK1).
-- **Population-specific methylation signature clusters** informed by AA vs EA tumour methylation studies.
-All records are **fully synthetic**, derived from literature-based distributions and not real methylation arrays.
-## Cohort design
-### Sample size and populations
-- **Total N**: 10,000 synthetic DNA methylation profiles.
-- **Populations**:
-  - `SSA_West`: 2,000
-  - `SSA_East`: 2,000
-  - `SSA_Central`: 1,500
-  - `SSA_Southern`: 1,500
-  - `AAW` (African American women): 1,500
-  - `EUR` (European reference): 1,000
-  - `EAS` (East Asian reference): 500
-Demographics mirror other Electric Sheep Africa breast cancer modules:
-- Predominantly **women**, with ~1% male breast cancers.
-- Ages 18–90, with **younger SSA** vs **older EUR/EAS/AAW** distributions.
-## Epigenetic age and age-acceleration
-Variables:
-- `epigenetic_age` – DNA methylation age (years).
-- `age_accel_years` – epigenetic age minus chronological age.
-- `age_accel_category` – `Decelerated`, `On_track`, `Accelerated`.
-Anchoring:
-- **Horvath 2013** (multi-tissue clock) and follow-up reviews show DNAm age tracks chronological age with **median error ≈3–5 years**.
-- Age acceleration (Δage) typically lies within **±5–10 years**, with modest shifts by lifestyle, disease, and ancestry.
-In this dataset:
-- `age_accel_years` is drawn from category-specific distributions (e.g., Decelerated ≈ -5 ± 2; Accelerated ≈ +5 ± 3).
-- SSA and AAW populations have slightly higher proportions of `Accelerated` than EUR/EAS, reflecting literature suggesting modest differences in DNAm age acceleration.
-## Global DNA methylation
-Variables:
-- `global_methylation_category` – `Low`, `Intermediate`, `High`.
-- `global_methylation_beta` – continuous 0–1 index (LINE-1/LUMA-like surrogate).
-Anchoring:
-- Studies of **LINE-1** and luminometric methylation suggest **global hypomethylation** in roughly **25–40%** of breast cancer patients, associated with genomic instability and poor prognosis.
-Configuration:
-- SSA and AAW populations have somewhat higher `Low` global methylation fractions than EUR/EAS, consistent with elevated hypomethylation prevalence in some series.
-- `global_methylation_beta` is drawn around category means (e.g., Low ≈0.55, Intermediate ≈0.70, High ≈0.82) with small noise.
-## Promoter methylation of tumour suppressor genes
-Genes modeled:
-- `BRCA1`
-- `RASSF1A`
-- `CDH1` (E-cadherin)
-- `CDKN2A_p16`
-- `DAPK1`
-Variables per gene (example for BRCA1):
-- `BRCA1_promoter_status` – `Unmethylated`, `Partially_methylated`, `Hypermethylated`.
-- `BRCA1_promoter_beta` – approximate promoter CpG beta value (0–1).
-Anchoring:
-- Multiple tumour series report **promoter CpG island hypermethylation** of BRCA1, RASSF1A, CDH1, CDKN2A/p16, and DAPK1 in **~20–50%** of breast cancers, often enriched in **ER-negative, high-grade, and TNBC** tumours.
-- Studies from the Carolina Breast Cancer Study and other cohorts note **racial differences** in promoter methylation (e.g., some loci more frequently methylated in tumours from African American women).
-In this dataset:
-- Per-gene promoter status distributions are defined **by population**, with slightly higher `Hypermethylated` fractions for selected genes (e.g., BRCA1, RASSF1A) in `SSA_*` and `AAW` groups vs `EUR/EAS`.
-- Beta values are drawn from status-based distributions that approximate Illumina 450K/EPIC behaviour:
-  - `Unmethylated` ≈0.1±0.05.
-  - `Partially_methylated` ≈0.5±0.10.
-  - `Hypermethylated` ≈0.8±0.07.
-## Population-specific methylation signatures
-Variables:
-- `methylation_signature_cluster` – `Cluster_A_EUR_like`, `Cluster_B_AAW_like`, `Cluster_C_SSA_like`.
-Anchoring:
-- **Genome-wide 450K analysis** of tumours from African American vs European American women (e.g., Oncotarget 2013; Carolina Breast Cancer Study) reveals **distinct CpG methylation patterns**, including differences in global methylation and promoter methylation at key loci.
-In this dataset:
-- `Cluster_A_EUR_like` is predominant in `EUR/EAS` populations, but present at lower frequency in AAW/SSA.
-- `Cluster_B_AAW_like` is enriched in `AAW`, but also present in SSA and some EUR/EAS tumours.
-- `Cluster_C_SSA_like` is more frequent in SSA populations, capturing a stylized SSA-associated methylation signature.
-These clusters are **latent summary labels** rather than actual high-dimensional profiles, but they are consistent with reported patterns of ancestry-associated methylation.
-## File and schema
-### `dna_methylation_data.parquet` / `dna_methylation_data.csv`
-Each row represents a synthetic tumour DNA methylation profile with:
-- **Demographics**
-  - `sample_id`
-  - `population`
-  - `region`
-  - `is_SSA`
-  - `is_reference_panel`
-  - `sex`
-  - `age`
-- **Epigenetic age**
-  - `epigenetic_age`
-  - `age_accel_years`
-  - `age_accel_category`
-- **Global methylation**
-  - `global_methylation_category`
-  - `global_methylation_beta`
-- **Promoter methylation (per gene)**
-  - `{GENE}_promoter_status`
-  - `{GENE}_promoter_beta`
-- **Population signature**
-  - `methylation_signature_cluster`
-## Generation
-The dataset is generated using:
-- `dna_methylation/scripts/generate_dna_methylation.py`
-with configuration in:
-- `dna_methylation/configs/dna_methylation_config.yaml`
-and literature inventory in:
-- `dna_methylation/docs/LITERATURE_INVENTORY.csv`
-Generation steps:
-1. **Demographic cohort** – multi-ancestry cohort with age and sex distributions by population.
-2. **Age acceleration** – assign `age_accel_category` by population and draw `age_accel_years` from Horvath/Raj-informed distributions; compute `epigenetic_age`.
-3. **Global methylation** – assign `global_methylation_category` by population and draw `global_methylation_beta` from category-specific distributions.
-4. **Promoter methylation** – for each gene, assign qualitative status by population and sample a corresponding beta value.
-5. **Signature clusters** – assign `methylation_signature_cluster` by population, reflecting AA/EA and SSA/EUR patterns reported in genome-wide methylation studies.
-## Validation
-Validation is performed with:
-- `dna_methylation/scripts/validate_dna_methylation.py`
-and summarized in:
-- `dna_methylation/output/validation_report.md`
-Checks include:
-- **C01–C02** – Sample size and population counts vs config.
-- **C03** – Age acceleration category distributions by population.
-- **C04** – Global methylation category distributions by population.
-- **C05** – Promoter methylation status distributions by population and gene.
-- **C06** – Methylation signature cluster distributions by population.
-- **C07** – Missingness across all key columns.
-An absolute deviation tolerance of **10%** is used for categorical distributions, and the released dataset is configured to reach an overall validation status of **`PASS`**.
-## Intended use
-This dataset is intended for:
-- **Methods development** in integrating DNA methylation with other SSA synthetic modules (pathology, IHC, immune, systemic markers, microbiome).
-- **Simulation studies** on epigenetic age, promoter methylation, and ancestry-associated methylation signatures.
-- **Educational use** in teaching epigenetic concepts in cancer.
-It is **not intended** for:
-- Estimating exact methylation frequencies or beta-value distributions for any specific locus or ancestry.
-- Direct clinical interpretation or patient-level risk prediction.
-## Ethical considerations
-- All methylation profiles are simulated; no real 450K/EPIC or bisulfite assay data are used.
-- Ancestry-related differences are modeled to reflect literature trends but must not be interpreted as deterministic or stigmatizing.
 ## License
-- License: **CC BY-NC 4.0**.
-- Free for non-commercial research, methods development, and education with attribution.
-## Citation
-If you use this dataset, please cite:
-> Electric Sheep Africa. "SSA Breast DNA Methylation Dataset (Women, Multi-ancestry, Synthetic)." Hugging Face Datasets.
-and relevant epigenetic literature (e.g., Horvath 2013, Horvath & Raj 2018, promoter methylation studies of BRCA1/RASSF1A/CDH1/CDKN2A, and genome-wide methylation analyses comparing African- and European-ancestry breast tumours).

 ---
+license: other
 language:
 - en
 task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
+- 10K<n<100K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "dna-methylation"
+- "epigenetic-clock"
+- "promoter-methylation"
+- "global-methylation"
+- "breast-cancer"
+- "sub-saharan-africa"
+- "cancer"
+pretty_name: "SSA Breast DNA Methylation Dataset (Women, Multi-ancestry) | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# SSA Breast DNA Methylation Dataset (Women, Multi-ancestry) | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `10K<n<100K` - **Formats:** `parquet` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-216-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+Dataset context from the existing Hugging Face card: SSA Breast DNA Methylation Dataset (Women, Multi-ancestry, Synthetic) Dataset summary This dataset provides a synthetic DNA methylation cohort conceptually linked to women with invasive breast cancer across multiple ancestry groups, with emphasis on Sub-Saharan Africa (SSA) and comparable reference populations. It captures high-level methylation features rather than site-level arrays, grounded in key literature on epigenetic clocks, promoter CpG methylation of tumour… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-cancer-ethiopia`](https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia) |
+| Sector | health |
+| Topic tags | dna-methylation, epigenetic-clock, promoter-methylation, global-methylation, breast-cancer, sub-saharan-africa |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `10K<n<100K` |
+| Countries | Ethiopia |
+| ISO3 coverage | `ETH` |
+| Last modified on HF | `2025-11-25 12:43:03+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-cancer-ethiopia")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** cc-by-nc-4.0
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia](https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
+## Citation
+```bibtex
+@misc{electric_sheep_africa_africa_cancer_ethiopia_2026,
+  title        = {SSA Breast DNA Methylation Dataset (Women, Multi-ancestry) | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-cancer-ethiopia}}
+}
+```
 ## License
+Released under cc-by-nc-4.0.
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
+---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.