divergence

divergence simplifies high-dimensional omics data into binary or ternary formats that encapsulate how data diverges from a specified baseline group with the same univariate or multivariate features.

Key Features:

• Divergence analysis: Performs divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PNAS 2018.
• Data digitization: Transforms high-dimensional omics data into binary or ternary formats.
• Baseline comparison: Quantifies divergence of samples from a specified baseline group.
• Feature flexibility: Supports both univariate and multivariate features for divergence assessment.

Scientific Applications:

• Omics data simplification: Reduces complexity of high-dimensional omics datasets for downstream analysis.
• Baseline-centered profiling: Enables digitization of omics profiles relative to a reference or control group.

Methodology:

Divergence analysis as described in Dinalankara et al, PNAS 2018.