MOVICS

MOVICS performs integrative clustering of multi-omics data using ten multi-omics integrative clustering algorithms to identify and characterize cancer molecular subtypes and provide standardized outputs for downstream analyses including model-free multiclass prediction applicable to external cohorts.

Key Features:

• Integrative clustering algorithms: Implements ten multi-omics integrative clustering algorithms for joint analysis of diverse omics datasets.
• Standardized outputs: Standardizes outputs from the clustering algorithms to enable consistent downstream analyses and comparability across datasets.
• Comprehensive downstream analyses: Performs characterization and comparison of identified subtypes and verifies subtype robustness using a model-free approach for multiclass prediction that can be applied to external cohorts.

Scientific Applications:

• Cancer molecular subtyping: Identifies and refines molecular subtypes to characterize tumor heterogeneity and associate subtypes with patient outcomes and therapeutic strategies.

Methodology:

Integrates genomics, transcriptomics, and proteomics data using multi-omics integrative clustering algorithms, standardizes algorithm outputs, and employs a model-free multiclass prediction approach for robustness verification and external cohort validation.