Datasets:

electricsheepafrica
/

africa-synth-population-tumor-gene-expression-african-all

@@ -1,342 +1,146 @@
 ---
 language:
 - en
-license: cc-by-nc-4.0
 task_categories:
 - tabular-classification
 - tabular-regression
-tags:
-- medical
-- oncology
-- breast-cancer
-- gene-expression
-- PAM50
-- Oncotype-DX
-- african-populations
-- synthetic-data
-- synthetic
 size_categories:
 - 10K<n<100K
-data_type: synthetic
----
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# Tumor Gene Expression Panels in African Breast Cancer
-**Dataset owner**: Electric Sheep Africa
-**Dataset type**: Synthetic tumor gene expression (Oncotype DX, PAM50, Ki-67)
-**Populations**: African and African-descent breast cancer patients
-**Version**: 1.0.0
-**License**: CC-BY-NC-4.0
----
-## 1. Dataset Description
-This dataset provides **synthetic tumor gene expression profiles** for **50,000** breast cancer cases from **African and African-descent populations**, designed to mirror:
-- **Oncotype DX–equivalent recurrence scores** for ER+/HER2− tumors
-- **PAM50 molecular subtypes** (Luminal A/B, Basal-like, HER2-enriched, Normal-like)
-- **Proliferation markers**, including **Ki-67** and related genes
-All records are **fully synthetic** and were generated using an **internal, literature-driven synthetic data methodology**, with parameters derived from peer-reviewed literature on African and African-descent breast cancer cohorts.
-> **Important**: This dataset contains *no real patient data*. It is derived entirely from literature-based distributions and coherence rules.
----
-## 2. Intended Use
-This dataset is intended for:
-- **Method development** for molecular subtyping (PAM50, IHC surrogates)
-- **Algorithm training** for Oncotype DX–like recurrence score prediction
-- **Proliferation and Ki-67 modeling** across African populations
-- **Health equity research** on differences in tumor biology by ancestry
-- **Education and benchmarking** in computational oncology
-Not intended for clinical decision-making or individual risk prediction.
----
-## 3. Populations & Cohort Design
-### 3.1 Populations
-The dataset includes 5 broad population groups, aligned with prior projects in this series:
-- **West_Africa** (e.g., Nigeria, Ghana, Senegal)
-- **East_Africa** (e.g., Kenya, Uganda, Ethiopia)
-- **Southern_Africa** (e.g., South Africa, Namibia, Botswana)
-- **Central_Africa** (e.g., Cameroon, DRC)
-- **African_American** (USA)
-### 3.2 Sample Size
-- **Total samples**: 50,000 synthetic tumors
-- **Approximate population fractions**:
-  - West_Africa: 25%
-  - East_Africa: 20%
-  - Southern_Africa: 15%
-  - Central_Africa: 10%
-  - African_American: 30%
----
-## 4. Molecular Features
-### 4.1 PAM50 Molecular Subtypes
-Each sample is assigned a **PAM50-like molecular subtype**:
-- **Luminal_A**
-- **Luminal_B**
-- **Basal_like**
-- **HER2_enriched**
-- **Normal_like**
-The distribution is **African-enriched for basal-like tumors**, reflecting the literature:
-- Basal_like: ~40%
-- Luminal_A: ~35%
-- Luminal_B: ~17%
-- HER2_enriched: ~8%
-- Normal_like: ~1–2%
-### 4.2 Receptor Status
-Immunohistochemistry-style receptor status is included:
-- `ER_status`: Positive / Negative
-- `PR_status`: Positive / Negative
-- `HER2_status`: Positive / Negative
-- `is_TNBC`: Triple-negative (ER−/PR−/HER2−)
-Triple-negative tumors (TNBC) are enriched (~35–40%) to reflect African cohorts.
-### 4.3 Gene Expression Features (Key Subset)
-All expression values are on a **log2(normalized expression + 1)** scale.
-This initial public version exposes a **key subset of genes** for transparency and compactness:
-- **Proliferation marker**:
-  - `MKI67_expr` – Ki-67
-- **Hormone receptor signaling**:
-  - `ESR1_expr` – Estrogen receptor
-  - `PGR_expr` – Progesterone receptor
-- **HER2 pathway**:
-  - `ERBB2_expr` – HER2 receptor
-- **Basal markers**:
-  - `KRT5_expr`, `KRT17_expr` – Basal cytokeratins
-Future versions may expose additional genes from the **Oncotype DX** and **PAM50** panels in a companion dataset.
-### 4.4 Proliferation (Ki-67)
-- `ki67_percentage` – Estimated Ki-67 labeling index (0–100%)
-- `ki67_category` – {`Low` (<14%), `Intermediate` (14–30%), `High` (>30%)}
-African and African-descent populations show **higher Ki-67**, particularly in basal-like and high-grade tumors.
-### 4.5 Oncotype DX–Equivalent Score
-For **ER+/HER2− tumors**, we provide a **simplified Oncotype DX–like recurrence score**:
-- `oncotype_RS` – Integer score from 0–100
-- `oncotype_risk_category` – {`Low`, `Intermediate`, `High`, `Not_applicable`}
-The risk distribution is calibrated from published literature on African American and multi-ethnic cohorts.
----
-## 5. Main File Schema
-### 5.1 `gene_expression_data.csv`
-- **Rows**: 50,000 samples
-- **Columns** (23 variables):
-Demographics & clinical:
-- `sample_id` – Synthetic ID (`BC_EXPR_00000` ...)
-- `population` – One of 5 population groups
-- `age` – Age in years (25–85)
-- `age_group` – {`<40`, `40-49`, `50-59`, `60+`}
-- `menopausal_status` – {`Premenopausal`, `Postmenopausal`}
-- `bmi` – Body mass index (kg/m²)
-- `tumor_grade` – {1, 2, 3}
-- `stage` – {`I`, `II`, `III`}
-Molecular subtypes and receptors:
-- `pam50_subtype` – {`Luminal_A`, `Luminal_B`, `Basal_like`, `HER2_enriched`, `Normal_like`}
-- `ER_status` – {`Positive`, `Negative`}
-- `PR_status` – {`Positive`, `Negative`}
-- `HER2_status` – {`Positive`, `Negative`}
-- `is_TNBC` – Boolean (triple-negative)
-Key gene expression values (log2 scale):
-- `MKI67_expr` – Ki-67
-- `ESR1_expr` – Estrogen receptor
-- `PGR_expr` – Progesterone receptor
-- `ERBB2_expr` – HER2 receptor
-- `KRT5_expr`, `KRT17_expr` – Basal cytokeratins
-Derived scores:
-- `ki67_percentage` – Ki-67 proliferation index (%)
-- `ki67_category` – {`Low`, `Intermediate`, `High`}
-- `oncotype_RS` – Oncotype-like recurrence score (0–100, NaN if not applicable)
-- `oncotype_risk_category` – {`Low`, `Intermediate`, `High`, `Not_applicable`}
----
-## 6. Data Access & Files
-### Main Dataset (root)
- - **`gene_expression_data.csv`** – 50,000 × 23 variables (main table, CSV)
- - **`gene_expression_data.parquet`** – Same table in Parquet format for efficient loading.
-### Auxiliary Files
-At this time, auxiliary summary tables and the full validation report are not distributed as separate files in this repository. Key validation findings and literature sources are summarized in this dataset card.
 ---
-## 7. Generation Methodology (Summary)
-The dataset was generated using an **internal, literature-driven synthetic data framework**, following a structured multi-phase pipeline:
-1. **Domain specification** – Define gene panels (Oncotype DX, PAM50, Ki-67) and African focus.
-2. **Literature review** – Extract subtype frequencies, Ki-67 levels, and score distributions from 30+ studies.
-3. **Parameter configuration** – Encode all parameters in YAML (`gene_expression_config.yaml`).
-4. **Generation** – Use a reproducible Python generator to simulate 50,000 cases.
-5. **Validation** – Run `scripts/validate_gene_expression.py` to check distributions, coherence, and correlations (30 checks).
-6. **Documentation** – Create dataset card and usage examples.
-7. **Release** – Upload to Hugging Face with CC-BY-NC-4.0.
-### 7.1 Key Modeling Choices
-- **PAM50 subtypes** assigned using population-specific target proportions.
-- **Receptor status** inferred from subtypes with high concordance (e.g., Basal_like ↔ TNBC).
-- **Gene expression values** drawn from subtype-specific normal distributions calibrated to literature and TCGA-like ranges.
-- **Ki-67** modeled as a function of subtype, population multiplier, and MKI67 expression.
-- **Oncotype DX scores** simulated for ER+/HER2− tumors using population and subtype–dependent distributions.
----
-## 8. Validation Status
-- This dataset has been validated using `scripts/validate_gene_expression.py` on the released CSV.
-- ✅ **Generation completed** for 50,000 samples.
-- ✅ **30 validation checks** executed (structure, distributions, coherence).
-- ✅ **Results**: 25 `PASS`, 5 `WARN`, 0 `FAIL`.
-- ✅ **Validation report**: maintained internally; main findings are summarized below.
-Key validation themes include:
-- Distributions vs literature (subtypes, TNBC, Ki-67, Oncotype)
-- Expression ranges and subtype-specific patterns
-- ER/ESR1 and HER2/ERBB2 concordance
-- Basal-like ↔ TNBC overlap
-- Ki-67 correlation with MKI67 expression and grade
----
-## 9. Example Usage
-### 9.1 Load with pandas
-```python
-import pandas as pd
-df = pd.read_csv("gene_expression_data.csv")
-print(df.shape)
-print(df.head())
-```
-### 9.2 Load with `datasets`
 ```python
 from datasets import load_dataset
-dataset = load_dataset("electricsheepafrica/tumor-gene-expression-african")
-df = dataset["train"].to_pandas()
-print(f"Samples: {len(df)}")
-print(f"Columns: {len(df.columns)}")
 ```
-### 9.3 Simple Analysis: PAM50 Subtypes
 ```python
-subtype_counts = df["pam50_subtype"].value_counts(normalize=True) * 100
-print(subtype_counts)
-```
-### 9.4 Ki-67 by Subtype
-```python
-ki67_by_subtype = df.groupby("pam50_subtype")["ki67_percentage"].describe()
-print(ki67_by_subtype)
 ```
-### 9.5 Oncotype DX Risk in ER+/HER2− Tumors
-```python
-mask = (df["ER_status"] == "Positive") & (df["HER2_status"] == "Negative")
-rs = df.loc[mask, "oncotype_RS"]
-print(rs.describe())
-print(df.loc[mask, "oncotype_risk_category"].value_counts(normalize=True) * 100)
-```
----
-## 10. Ethical & Appropriate Use
-### 10.1 Appropriate Uses ✅
-- Method development and benchmarking
-- Educational demos and tutorials
-- Health equity and disparity analysis (synthetic)
-- Robustness testing for molecular classifiers
-### 10.2 Inappropriate Uses ❌
-- Clinical decision-making for individual patients
-- Real-world prognosis or treatment selection
-- Insurance, employment, or financial decisions
-- Genetic ancestry inference at the individual level
-This dataset is **synthetic** and must not be used as a substitute for real clinical or genomic data in patient care.
----
-## 11. License
-- **License**: CC-BY-NC-4.0
-- **Commercial use**: Not permitted without explicit permission.
-If you use this dataset in academic or non-commercial work, please cite as below.
----
-## 12. Citation
-```text
-Electric Sheep Africa (2025).
-Tumor Gene Expression Panels in African Breast Cancer (Synthetic Dataset).
-Generated using an internal, literature-driven synthetic data methodology.
-Hugging Face Datasets. Version 1.0.0.
-```
 ---
-## 13. Contact
-- **Organization**: Electric Sheep Africa
-- **Hugging Face**: https://huggingface.co/electricsheepafrica
-Feedback and collaboration inquiries are welcome.

 ---
+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"
+- "medical"
+- "oncology"
+- "breast-cancer"
+- "gene-expression"
+- "pam50"
+- "oncotype-dx"
+- "african-populations"
+- "synthetic-data"
+- "synthetic"
+- "cancer"
+pretty_name: "Africa Synth Population Tumor Gene Expression African All | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Africa Synth Population Tumor Gene Expression African All | 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-109-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: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Tumor Gene Expression Panels in African Breast Cancer Dataset owner: Electric Sheep AfricaDataset type: Synthetic tumor gene expression (Oncotype DX, PAM50, Ki-67)Populations: African and African-descent breast cancer patientsVersion: 1.0.0License: CC-BY-NC-4.0 1. Dataset Description This dataset provides synthetic tumor… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all) |
+| Sector | health |
+| Topic tags | medical, oncology, breast-cancer, gene-expression, PAM50, Oncotype-DX, african-populations, synthetic-data, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:38:41+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-synth-population-tumor-gene-expression-african-all")
+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: country, 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-synth-population-tumor-gene-expression-african-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all)
+- **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_synth_population_tumor_gene_expression_african_all_2026,
+  title        = {Africa Synth Population Tumor Gene Expression African All | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-population-tumor-gene-expression-african-all}}
+}
+```
+## 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`.