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A TESTING BASED APPROACH TO THE DISCOVERY OF DIFFERENTIALLY CORRELATED VARIABLE SETS
By Kelly Bodwin1, Kai Zhang1, Andrew Nobel1
1The University of North Carolina at Chapel Hill.
The Annals of Applied Statistics
|December 25, 2019
Summary
Differential Correlation Mining (DCM) identifies variable sets with higher average correlation in one condition versus another. This method aids in analyzing differences in second-order behavior across datasets, applicable to genomics and brain imaging.
Area of Science:
- Computational Biology
- Data Mining
- Statistical Analysis
Background:
- Identifying variables with differing behavior across conditions is crucial in data analysis.
- Second-order behavior, such as correlations between variables, offers insights into system dynamics.
Purpose of the Study:
- Introduce Differential Correlation Mining (DCM), a novel method for identifying differentially correlated sets of variables.
- To detect variable sets exhibiting higher average pairwise correlation under one sampling condition compared to another.
Main Methods:
- DCM employs an iterative search procedure to adaptively refine candidate variable sets.
- Hypothesis testing on individual variables, using asymptotic distributions of average differential correlation, guides set updates.
Main Results:
- The study demonstrates DCM's capability to find sets of variables with condition-specific correlation patterns.
- Performance was validated using simulated data and real-world datasets from genomics and brain imaging.
Conclusions:
- DCM provides an effective approach for differential analysis of second-order variable behavior.
- The method is broadly applicable to diverse scientific fields requiring comparative data analysis.
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