rScudo

rScudo implements rank-based sample-specific signatures to classify molecular profiles for diagnostic, prognostic, and investigational analyses.

Key Features:

• Rank-Based Signature Analysis: Extracts and compares sample-specific rank-order signatures of molecular features, prioritizing relative ranks over absolute expression values.
• Similarity-Based Classification: Uses similarity metrics of rank-based signatures to perform clustering and classification of samples.
• Validation in SBV IMPROVER: Performance and robustness were validated through direct comparison with existing methods in the SBV IMPROVER Diagnostic Signature Challenge.
• Applicability to Molecular Profiles: Applicable to diverse molecular profile types including gene expression, transcriptomics, and proteomics datasets.
• Comparative Sensitivity: Enhances sensitivity to variation that may be overlooked by traditional differential expression analyses.

Scientific Applications:

• Diagnostic Purposes: Classifies disease-specific molecular signatures to support diagnostic studies.
• Prognostic Analysis: Detects subtle molecular differences to inform prognosis and potential treatment outcome studies.
• Investigational Research: Enables exploration of gene expression and other molecular phenomena using rank-based signature comparisons.

Methodology:

Extraction of sample-specific rank-based signatures by ordering molecular features within each sample and comparing signature similarity for clustering and classification; results were compared against traditional differential expression analyses and validated in the SBV IMPROVER Diagnostic Signature Challenge.