Greedy Projected Distance Correlation

Using projected distance correlation to build a conditional dependency graph among high-dimensional mixed data can reduce the computational cost, which is critical for analyzing large volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with G-PDC than distance correlation, Pearson's correlation, and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies.

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