---
language:
- en
tags:
- sequencing-qc
- batch-effects
- rna-quality
- dna-quality
- ngs
- breast-cancer
- sub-saharan-africa
license: cc-by-nc-4.0
pretty_name: SSA Breast Data Quality Benchmarks (Sequencing & Sample QC)
task_categories:
- other
size_categories:
- 1K<n<10K
---

# SSA Breast Data Quality Benchmarks (Sequencing & Sample QC, Synthetic)

## Dataset summary

This module provides a **synthetic data quality benchmark dataset** for breast cancer sequencing studies, focusing on:

- **Batch effects and inter-laboratory variation** (sequencing center, platform, batch, run year).
- **RNA quality** metrics (RNA integrity number, yield, read depth, %Q30) and QC flags.
- **DNA quality** metrics (yield, coverage, %≥20x, duplication, contamination) and QC flags.

It is designed to support **method development and teaching** around technical variation, QC pipelines, and batch-effect correction, especially in **multi-ancestry** contexts including Sub-Saharan Africa.

All records are **fully synthetic**, parameterized from published multi-center NGS performance studies, RIN literature, and FFPE vs fresh-frozen comparisons.


## Cohort design

### Sample size and populations

- **Total N**: 8,000 synthetic sequencing libraries.
- **Populations**:
  - `SSA_West`: 1,600
  - `SSA_East`: 1,600
  - `SSA_Central`: 1,200
  - `SSA_Southern`: 1,200
  - `AAW` (African American women): 1,200
  - `EUR`: 800
  - `EAS`: 400

- **Sex**:
  - `Female` ≈95%, `Male` ≈5% (to allow mixed-sex QC modeling).

This dataset is **not** intended to represent incidence or prevalence; it is a QC sandbox for realistic multi-ancestry technical variation.


### Sequencing centers and platforms

Variables:

- `sequencing_center`:
  - `Lab_A_EUR` (European reference center)
  - `Lab_B_USA` (US reference center)
  - `Lab_C_EAS` (East Asian reference center)
  - `Lab_SSA_1`, `Lab_SSA_2` (SSA regional centers)

- `sequencing_platform`:
  - `HiSeq4000`
  - `NovaSeq6000`
  - `NextSeq550`

Center assignment depends on population (e.g., SSA samples enriched in `Lab_SSA_1/2`; EUR samples enriched in `Lab_A_EUR`; EAS in `Lab_C_EAS`). Platforms differ by center (e.g., more NovaSeq in reference centers, more HiSeq in SSA labs).

Additional technical variables:

- `library_batch` – integer batch identifier (1–12).
- `run_year` – uniform across 2012–2024 to emulate protocol drift / upgrades.


## RNA quality metrics

For each sample, RNA metrics are present with probability `has_rna_prob ≈ 0.85` (some libraries are DNA-only).

Variables:

- `has_rna` – boolean.
- `rna_source` – `FFPE`, `Fresh_frozen`, `Blood_PAXgene` (distribution by center).
- `rna_rin` – RNA Integrity Number (1–10 scale):
  - Base mean around ~7.2 with SD ~1.0.
  - Source effects: `FFPE` ≈ −2, `Fresh_frozen` ≈ +0.4, `Blood_PAXgene` ≈ +0.2.
  - Center effects: e.g., `Lab_A_EUR` slightly higher RIN than `Lab_SSA_1/2`.
- `rna_yield_ng` – expected input RNA mass (log-normal distribution with source effects).
- `rna_read_count_millions` – approximate RNA-seq depth:
  - Base ~40M reads, with platform effects (e.g., more reads on NovaSeq than NextSeq).
- `rna_pct_q30` – % bases at Q30 or higher:
  - Base ~90%, with platform and center shifts reflecting inter-lab variation.

QC flags based on thresholds (RIN ≥6.5, ≥20M reads, ≥80% Q30):

- `rna_qc_rin_pass`
- `rna_qc_read_count_pass`
- `rna_qc_q30_pass`
- `rna_qc_pass` – overall RNA QC pass (all criteria met).


## DNA quality metrics

For each sample, DNA metrics are present with probability `has_dna_prob ≈ 0.90`.

Variables:

- `has_dna` – boolean.
- `dna_source` – `FFPE`, `Fresh_frozen`, `Blood` (distribution by center).
- `dna_yield_ng` – DNA input mass (log-normal with source effects; FFPE yields lower on average).
- `dna_mean_coverage` – mean on-target coverage:
  - Base ~80×, lower for FFPE and SSA centers, higher for fresh-frozen and reference centers.
- `dna_pct_bases_20x` – % of target bases with ≥20× coverage (base ~92%).
- `dna_duplication_rate` – fraction of duplicated reads:
  - Base ~0.15, elevated in FFPE and some SSA centers; slightly reduced in reference labs.
- `dna_contamination_pct` – estimated sample contamination (%), generally low (~1%).

QC flags based on thresholds (e.g., coverage ≥30×, ≥80% bases at ≥20×, duplication ≤0.25, contamination ≤5%):

- `dna_qc_coverage_pass`
- `dna_qc_pct20x_pass`
- `dna_qc_duplication_pass`
- `dna_qc_contamination_pass`
- `dna_qc_pass` – overall DNA QC pass (all criteria met).


## Technical variation & batch effects

The dataset encodes **several layers of technical variation**:

- **Center-level shifts**:
  - Higher mean RIN and Q30 in `Lab_A_EUR` and `Lab_B_USA` than in `Lab_SSA_1/2`.
  - Higher mean coverage and lower duplication in reference centers; more variable metrics in SSA labs.

- **Platform effects**:
  - NovaSeq oriented towards higher depth and slightly higher Q30.
  - NextSeq with somewhat lower depth/Q30, capturing platform-typical performance.

- **Source effects**:
  - FFPE samples have lower RIN, higher duplication, and slightly lower effective coverage.
  - Fresh-frozen samples have higher coverage and lower duplication.

These patterns follow the literature on **batch effects**, **ABRF NGS platform benchmarks**, and **FFPE vs fresh-frozen DNA/RNA**.


## Files and schema

### Data table

Files:

- `data_quality.parquet`
- `data_quality.csv`

Each row is one library/sample with:

- **Demographics & identifiers**:
  - `sample_id`
  - `population`, `region`, `is_SSA`, `is_reference_panel`
  - `sex`

- **Technical factors**:
  - `sequencing_center`
  - `sequencing_platform`
  - `library_batch`
  - `run_year`

- **RNA metrics**:
  - `has_rna`
  - `rna_source`
  - `rna_rin`
  - `rna_yield_ng`
  - `rna_read_count_millions`
  - `rna_pct_q30`
  - `rna_qc_rin_pass`
  - `rna_qc_read_count_pass`
  - `rna_qc_q30_pass`
  - `rna_qc_pass`

- **DNA metrics**:
  - `has_dna`
  - `dna_source`
  - `dna_yield_ng`
  - `dna_mean_coverage`
  - `dna_pct_bases_20x`
  - `dna_duplication_rate`
  - `dna_contamination_pct`
  - `dna_qc_coverage_pass`
  - `dna_qc_pct20x_pass`
  - `dna_qc_duplication_pass`
  - `dna_qc_contamination_pass`
  - `dna_qc_pass`

For samples without RNA or DNA (`has_rna=False` or `has_dna=False`), the corresponding metrics are `NaN` and QC flags are `False`.


## Generation

The dataset is generated using:

- `data_quality_benchmarks/scripts/generate_data_quality.py`

with configuration in:

- `data_quality_benchmarks/configs/data_quality_config.yaml`

and literature inventory in:

- `data_quality_benchmarks/docs/LITERATURE_INVENTORY.csv`

Key generation logic:

1. **Base sample table** – multi-ancestry populations, sex, center, platform, batch, run year.
2. **RNA QC assignment** – conditional on `has_rna` and driven by source, center, and platform:
   - RIN values and yields are drawn from normal/log-normal distributions with additive effects.
   - Depth and Q30 reflect platform and center performance.
   - RIN/read count/Q30 thresholds define pass/fail flags.
3. **DNA QC assignment** – conditional on `has_dna`:
   - Yield, coverage, %≥20x, duplication, and contamination vary by source and center.
   - Coverage, %≥20x, duplication, and contamination thresholds define DNA QC pass/fail.


## Validation

Validation is performed with:

- `data_quality_benchmarks/scripts/validate_data_quality.py`

and summarized in:

- `data_quality_benchmarks/output/validation_report.md`

Checks include:

- **C01–C02** – Sample size and population counts vs configuration.
- **C03** – Sequencing center distributions by population.
- **C04** – RNA RIN distribution mean vs expected.
- **C05** – RNA QC pass rate within a target range (not too permissive, not too strict).
- **C06** – DNA coverage and duplication patterns for `FFPE` vs `Fresh_frozen` (Fresh_frozen higher coverage, FFPE higher duplication).
- **C07** – DNA QC pass rate within a target range.
- **C08** – Center effect magnitude: RIN and coverage differences between best and worst centers meet minimum thresholds.
- **C09** – Missingness in key variables (demographics, technical factors, QC metrics).


## Intended use

This dataset is intended for:

- **Developing and testing QC pipelines** for RNA-seq and DNA-seq.
- **Benchmarking batch effect detection and correction** methods.
- **Teaching** about:
  - Inter-laboratory variation and its impact on downstream analyses.
  - The role of RIN, coverage, duplication, and contamination in sequencing QC.
  - How technical covariates (center, platform, batch) interact with biological covariates (population).

It is **not intended** for:

- Estimating actual failure rates of real-world labs.
- Drawing conclusions about specific centers or platforms.
- Clinical QC decision-making for patient samples.


## Ethical considerations

- All data are **synthetically generated** from literature-based parameter sets; no real sample identifiers or QC runs are used.
- Center and platform labels are generic; they must **not** be interpreted as referring to specific institutions.
- The dataset aims to enable more robust analyses under technical variability, not to evaluate real labs.


## License

- License: **CC BY-NC 4.0**.
- Free for non-commercial research, method development, and education with attribution.


## Citation

If you use this dataset, please cite:

> Electric Sheep Africa. "SSA Breast Data Quality Benchmarks (Sequencing & Sample QC, Synthetic)." Hugging Face Datasets.

and, where applicable, key QC/batch-effect references such as Leek et al. (2010), Chen et al. (2011), ABRF NGS performance studies, and RIN/FFPE quality literature.