When large omics datasets present unwanted latent variability, a critical analysis step is to control these so-called batch effects properly. However, most batch effect-correction algorithms (BECAs) face limitations when the source of unwanted variation and the variable of interest are confounded. In this paper, we use RNA-seq data to study the effects of radiation contamination on tree frogs (Hyla orientalis) collected in the Chornobyl Exclusion Zone. We identify the site of collection of the frogs as a confounding factor in the transcriptomics analysis. We present our strategy to correct this confounding effect using the following BECAs: ComBat-seq, linear residualization, and Surrogate Variable Analysis. We show that the severe confounding between the site and radiocontamination level makes the correction step challenging. Instead, we investigate the site-to-site variability and successfully deconvolute the batch variable from the radiation level by adjusting for the population genetic structure. Our strategy allowed us to reveal the effects of low-dose radiation on the gene expression of Chornobyl tree frogs and appropriately preprocess the RNA-seq dataset for future multimodal integrative analyses.