StratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Cancer subtype identification relies on analyzing heterogeneous genomics data.
• Current methods use inefficient ad-hoc scripts and static plots, limiting data exploration.

Purpose of the Study:

• To develop StratomeX, an integrative visualization tool for exploring cancer subtypes across multiple genomics datasets.
• To address the inefficiency and limitations of current subtype analysis methods.

Main Methods:

• StratomeX visualizes datasets as columns and subtypes as bricks, using ribbons to connect subtype relationships across data types.
• Features include drill-down capabilities, small multiples for assessing pathway and survival impacts, and a meta-visualization interface for configuration.
• Developed in collaboration with domain experts.

Main Results:

• StratomeX facilitates efficient exploration of relationships between candidate subtypes across diverse genomic data (gene expression, DNA methylation, copy number).
• The tool enables assessment of functional and clinical implications, such as effects on molecular pathways and patient survival.
• Case studies demonstrate efficient replication of literature findings and generation of novel insights.

Conclusions:

• StratomeX enhances the integrative analysis of heterogeneous genomics datasets for cancer subtype discovery.
• The tool improves efficiency and visual exploration, aiding researchers in gaining deeper insights into cancer biology and clinical outcomes.