YPL67/BC

Breast Cancer Gene Expression DMFS Dataset

Dataset Summary

This dataset is derived from public breast cancer gene-expression datasets distributed through Bioconductor and used in the genefu breast cancer analysis framework.

The original data and processing workflow are documented in the Bioconductor genefu vignette: https://www.bioconductor.org/packages/devel/bioc/vignettes/genefu/inst/doc/genefu.html

The raw data are not distributed as standalone CSV files. Instead, they are programmatically accessed through Bioconductor experimental data packages and processed in R before being exported and further processed in Python.

This Hugging Face repository organizes the data into two levels:

1. Intermediate Data: CSV files exported from the initial R/Bioconductor processing step.
2. Processed Data: model-ready train, validation, and test splits generated in Python.
3. Processed Data Subset: subset of the processed rain, validation, and test splits generated in Python. These have a reduced feature space and are for display only.

The prediction task is binary classification of distant metastasis-free survival (DMFS) event status using gene-expression features.

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Source

This dataset is derived from publicly available breast cancer gene-expression cohorts distributed via Bioconductor and accessed using the genefu framework.

For full details on the original data sources and preprocessing pipeline, refer to the genefu website provided above.

The underlying cohorts include datasets such as:

• MAINZ
• TRANSBIG

These datasets are combined and processed using Bioconductor tools including genefu and Biobase.

Users are encouraged to consult the original source for detailed documentation of cohort composition, biological context, and preprocessing methodology.

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Initial R Processing (Bioconductor)

The intermediate data provided in this repository are generated using an R-based preprocessing pipeline.

Key steps include:

• Loading required libraries:
  • genefu
  • Biobase
  • Bioconductor breast cancer datasets
• Loading datasets:
  • breastCancerMAINZ
  • breastCancerTRANSBIG
• Removing phenotype columns in MAINZ that are entirely missing
• Combining datasets using Biobase::combine
• Extracting:
  • Phenotype data via pData
  • Gene-expression data via exprs
• Filtering samples with available DMFS information:
  • Non-missing t.dmfs
  • Non-missing e.dmfs
• Transposing the expression matrix so rows correspond to samples
• Constructing a cohort table with:
  • sample_id
  • t_dmfs
  • e_dmfs
  • dmfs_label

Label Definition

The binary classification label is defined as:

• dmfs_label = 1 if e_dmfs == 1
• dmfs_label = 0 otherwise

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Repository Structure

BC/
├── intermediate_data/
│   ├── cohort.csv
│   └── dataset.csv
│
├── processed_data/
│   ├── train.csv
│   ├── validation.csv
│   └── test.csv
│
├── processed_data_subset/
│   ├── train.csv
│   ├── validation.csv
│   └── test.csv
│
└── README.md