omicade4

omicade4 performs multiple co-inertia analysis (MCIA) to integrate and compare multi-omics datasets, identifying co-relationships across high-dimensional omics data for biological interpretation.

Key Features:

• Covariance Optimization: Employs a covariance optimization criterion to project multiple datasets into a unified dimensional space.
• Feature Extraction and Biological Interpretation: Extracts the most variant components from each dataset to support biological interpretation and pathway analysis.
• Platform Independence: Operates independently of feature annotation, enabling extraction of informative features not consistently present across datasets.
• Graphical Representations: Produces graphical representations to visualize relationships and co-variances between large datasets.

Scientific Applications:

• Integration of Transcriptome and Proteome Profiles: Integrated transcriptome and proteome profiles in the NCI-60 cell line panel to reveal complementary features and enhance pathway coverage, including pathways such as leukemia extravasation signaling.
• Comparison Across Platforms: Compared transcriptome profiles from microarray platforms and next-generation RNA sequencing in high-grade serous ovarian tumors to identify the most informative platform and extract robust biomarkers of molecular subtypes.

Methodology:

Multiple co-inertia analysis (MCIA) using a covariance optimization criterion to simultaneously project multiple datasets into a common low-dimensional space and extract the most variant components highlighting co-variant patterns.