consensusOV

consensusOV integrates multiple high-grade serous ovarian carcinoma (HGSOC) gene-expression subtype classifiers to generate robust consensus subtype assignments for molecular characterization and clinical correlation.

Key Features:

• Implementation of Major Subtype Classifiers: Incorporates four major HGSOC gene-expression subtype classifiers to categorize tumors into distinct molecular subtypes.
• Consensus Classifier: Consolidates outputs from multiple subtype classification methods to produce a unified consensus assignment.
• Statistical Robustness Assessment: Uses meta-analysis of publicly available expression data to evaluate concordance and statistical agreement among classifiers.
• Confidence Scoring: Provides a confidence score for each consensus classification to indicate assignment certainty.
• Analysis of Unclassifiable Tumors: Identifies and examines tumors that remain unclassifiable under current subtype frameworks.

Scientific Applications:

• Improved Patient Stratification: Enables more reliable stratification of HGSOC patients into molecular subtypes for research and translational studies.
• Correlation with Clinical Outcomes: Associates subtype assignments with clinical parameters including patient age, overall survival, tumor purity, and lymphocyte infiltration.
• Facilitation of Targeted Therapies: Supports identification of candidate patients for targeted therapeutic approaches based on robust molecular subtyping.

Methodology:

Performs meta-analysis of gene expression data from multiple datasets, assesses pairwise concordance among subtype classifiers and their association with overall survival, and trains the consensus classifier on samples consistently classified across different methods.