In this work, we propose a copula-based method to generate synthetic
gene expression data that account for marginal and joint probability distributions
features captured from real data. Our method allows us to implant significant
genes in the synthetic dataset in a controlled manner, giving the possibility of
testing new detection algorithms under more realistic environments
