A Comprehensive Preprocessing Pipeline for TCGA-BRCA Multi-Omics Data Integration

The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) multi-omics cohort faces significant integration challenges due to fragmented data distribution, varying identifiers, and pronounced batch effects. We present a comprehensive preprocessing pipeline unifying RNA-seq, DNA methylation, CNV, and clinical data into analysis-ready matrices. Our pipeline orchestrates established methods including ComBat for batch effect correction and TPM normalization for expression data. We achieved four-fold reduction in technical variability while preserving biological signals, demonstrated through strong expression-methylation anti-correlations and 89% PAM50 subtype classification accuracy. The resulting high-fidelity dataset contains 710 quality-controlled patients across 17,014 genes, providing 20% larger sample size and 70% greater gene coverage compared to existing resources. This fully documented framework establishes a standardized foundation for reproducible multi-omics research in biomarker discovery, molecular subtyping, and survival prediction.