# PGK1 TCGA-BRCA data and code (Online Resource 2)

This archive accompanies the Analysis article **PGK1 expression across molecular subtypes and overall survival in breast cancer: an exploratory TCGA-BRCA analysis**.

## Data provenance

All analyses use official cBioPortal DataHub files for study `brca_tcga_pan_can_atlas_2018`:

- `data_mrna_seq_v2_rsem.txt`
- `data_clinical_patient.txt`
- `data_clinical_sample.txt`

The two clinical source files are included in `source_data/`. The approximately 148 MB RNA-seq source file is not redistributed; its exact filename, source URL, and SHA-256 are listed in `SOURCE_MANIFEST.csv`.

No simulated or imputed expression values were used.

## Reproduce all reported results

Create a Python environment and install the pinned dependencies:

```bash
python -m pip install -r requirements.txt
python scripts/run_all_analyses.py
```

The script regenerates:

- PAM50 summary, one-way ANOVA, and Tukey comparisons
- complete-case cohort characteristics and reverse Kaplan-Meier follow-up
- all three Cox models and proportional-hazards tests
- Kaplan-Meier/log-rank summary
- panel-gene correlations and composite-signature scores
- all numerical tables and both main figures

Outputs are written to `results/` and `figures/`.

## Audit from the complete official RNA-seq file

Place the downloaded `data_mrna_seq_v2_rsem.txt` in `source_data/`, verify its SHA-256 against `SOURCE_MANIFEST.csv`, and run:

```bash
python scripts/prepare_from_raw.py
```

This rebuilds the curated expression subset from the official RSEM file.

## Interpretation limits

- Retrospective single-cohort analysis without independent validation
- Composite signatures are mean standardized expression scores, not direct biochemical pathway-activity measurements
- Median expression groups are used only for Kaplan-Meier visualization; continuous standardized PGK1 is used for inference
