Unbiased distance correlation with sample-size-aware confidence bounds for comparative omics network analysis.

Introduction: Data-driven determination is a powerful approach for unbiased investigation of the functional relationships in biomolecular networks. Such networks can be inferred from omics data, where correlation analysis is a commonly used method. However, the correlation values depend strongly on sample variability and size of the sample set in the general case, leading to unstable results and possibly highly erroneous conclusions. Methods: In this work, we show that similar to the Pearson and Spearman correlation approaches, distance correlation as a general non-linear polytonic correlation method also depends on the sample size. We show that both the Results: We integrated bias-corrected distance correlation with sample-size-matched bootstrapping, chi-squared Conclusion: We present a method for unbiased distance correlation network derivation with permutations and comparisons between the sample groups. All approaches presented here are available through an online application (https://www.insilicobiology.ca/shiny/sidco+/), and all related code is available at https://github.com/computationalmetabolomicsca/sidco_plus.