Mathematical modelling and effect size analysis in support of searching for the proteomic signature of radiotherapy toxicity

Development of new technologies has resulted in the significant expansion of biological research, among which studies in the area of genomics, transcriptomics, proteomics, and metabolomics are the leading ones. In the majority of omics studies, the goal is to identify reliable molecular biomarkers and pathways associated with the examined process. In almost all cases, a list of differentially expressed genes or proteins is constructed, which is not easy to obtain for some experimental designs. In our work, we mainly focus on the experiments with small sample size. The goal was to determine the robust proteomic signature of radiation exposure in the mouse model. Our selection algorithm combines mathematical modelling of signal and its fold change distributions with the comprehensive effect size analysis. Thanks to the data-driven automated thresholding of the protein absolute or relative (fold change) expressions, and Cohens effect size based filters, the obtained proteomic signature demonstrated a higher level of consistency and functional coherency. The additional, intuitively expected, signalling pathways were identified when compared to the standard statistical approach.