ProcMod

ProcMod estimates correlations and shared variation between high-dimensional data matrices to quantify inter-dataset relationships in molecular biology and ecology.

Key Features:

• Procrustean Correlation Coefficient Correction: Implements a corrected Procrustean correlation coefficient tailored for high-dimensional data to mitigate false correlations from classical approaches.
• Shared Variation Estimation: Estimates the shared variation between two datasets using the corrected Procrustean metric.
• Partial Correlation Coefficients: Computes partial correlation coefficients among matrix datasets to control for confounding factors and assess direct relationships between variables.

Scientific Applications:

• Molecular biology: Quantifying shared variation and correlations between high-dimensional molecular datasets.
• Ecology: Informing and improving ecological models by providing robust estimates of shared variation among ecological data matrices.
• Disentangling complex biological interactions: Separating direct from indirect relationships in multivariate data to clarify biological interactions.
• Identifying hidden patterns: Detecting patterns and correlations that may be obscured by traditional methods.

Methodology:

Applies a refined Procrustean approach by correcting the Procrustean correlation coefficient to estimate shared variation between matrices and computes partial correlation coefficients among matrix datasets.