PROMO

PROMO analyzes and visualizes large-scale, clinically labeled multi-omic cancer datasets to enable molecular subtype stratification, prognostic biomarker identification, survival analysis, and classifier generation.

Key Features:

• Data import and preprocessing: Importing and preprocessing of multi-omic datasets from high-throughput technologies.
• Visualization techniques: Visualization methods to aid interpretation of complex multi-omic and clinical data.
• Clustering algorithms: Clustering algorithms for grouping similar samples and revealing molecular patterns.
• Clinical label enrichment testing: Statistical testing for enrichment of clinical labels among molecular groups.
• Survival analysis: Survival analysis to evaluate prognostic factors and associations with outcomes.
• Multi-omic data support (genomics, transcriptomics, proteomics): Handling datasets derived from one or multiple high-throughput technologies including genomics, transcriptomics, and proteomics.
• Clinical-genomic integration: Integration of clinical information with genomic and other omic data for combined analyses.
• Stratification and subtype discovery: Stratifying tumor samples into molecular subtypes with clinical relevance.
• Biomarker identification: Identification of prognostic biomarkers for predictive and prognostic studies.
• Classifier generation: Generation of classifiers to categorize new samples based on established molecular subtypes.

Scientific Applications:

• Cancer subtype discovery: Identifying novel molecular subtypes of tumors through multi-omic clustering and integration.
• Biomarker discovery and prognostic studies: Detecting biomarkers that predict disease progression or treatment response and assessing prognostic value.
• Molecular classifier development: Developing classifiers for diagnostic or prognostic categorization of samples based on molecular signatures.

Methodology:

Computational steps explicitly include importing and preprocessing multi-omic data, visualization, clustering, clinical label enrichment testing, survival analysis, statistical testing, classifier generation, and integration of clinical and molecular data.