Summary

This PR extends the supplemental data formatting workflow by introducing a schema-aware column mapping pipeline for heterogeneous supplemental datasets.

Added Features

• Intelligent column normalization
• Schema-aware fuzzy column mapping using RapidFuzz
• Automatic schema detection logic
• Validation for required schema fields
• Modularized formatting pipeline architecture
• CLI-based processing workflow

New Components

• mapper.py
• schemas.py
• validator.py

Example

Input columns:

• Tumor Sample Barcode
• Gene Name
• Patient Identifier

Automatically mapped to:

• SAMPLE_ID
• HUGO_SYMBOL
• PATIENT_ID

Goal

This contribution moves the formatter toward a reusable schema-driven supplemental data curation pipeline and reduces manual preprocessing effort for curators.