MuSA-r

MuSA-r integrates multi-omics (genomics, transcriptomics, proteomics) and radiomic data to enable joint radiogenomic analyses that link imaging features with molecular profiles for cancer research.

Key Features:

• Integrated Data Structure: Leverages the MultiAssayExperiment (MAE) framework to combine genomics, transcriptomics, proteomics, and radiomic data into a cohesive data object.
• Modular Architecture: Organized into distinct Pre-processing and Downstream Analysis modules to separate data preparation from analytical workflows.
• Pre-processing: Implements data filtering and normalization to prepare multi-omics and radiomic datasets for analysis.
• Downstream Analysis: Provides correlation analysis, clustering (including heatmap generation), and feature selection methods for integrated dataset exploration.

Scientific Applications:

• Cancer research: Facilitates integration of imaging and molecular data to study tumor biology, improve diagnostic and prognostic assessments, and support treatment personalization by correlating radiomic features with genomics, transcriptomics, and proteomics.

Methodology:

Combines diverse omics and radiomic datasets using the MultiAssayExperiment (MAE) framework, applies data filtering and normalization in pre-processing, and performs correlation analysis, clustering (heatmap generation), and feature selection in downstream analysis.