Divergence

Divergence transforms continuous omics measurements into digitized binary or ternary codes that quantify deviation from a baseline population for univariate and multivariate sample-level analyses.

Key Features:

• Digitization Framework: Converts each continuous entry of an omics profile into binary or ternary codes based on the degree of deviation from a predefined baseline population.
• Univariate and Multivariate Analysis: Supports digitization and analysis at both single-feature and multifeature levels.
• Sample-Level Analysis: Enables analysis and interpretation at the individual-sample level across datasets.
• Platform Versatility: Applicable across multiple omics platforms including genomics, transcriptomics, proteomics, and metabolomics.

Scientific Applications:

• Genomics: Detects genomic measurements that deviate from baseline distributions for downstream analyses.
• Transcriptomics: Identifies transcript-level departures from baseline expression profiles using digitized codes.
• Proteomics: Captures protein-level deviations by representing continuous abundance values as discrete codes.
• Metabolomics: Represents metabolite measurements as deviation-based discrete codes to simplify high-dimensional data.
• Cancer genomics (TCGA): Applied to Cancer Genome Atlas datasets to identify sample- and feature-level deviations relevant to cancer research.

Methodology:

Define a baseline population; evaluate each omics entry's deviation from that baseline; convert entries into binary or ternary digitized codes; support digitization at univariate and multivariate levels.