Comparing cellular response to two radiation treatments based on key features visualization

In modern treatment by radiotherapy, different irradiation modalities can be used, potentially producing different amounts of adverse effects. The differences between these modalities are often studied via two-sample time course in vitro experiments. The resulting data may be of high complexity, in which case simple methods are unadapted for extracting all the relevant information. In this article we introduce network-based tools for the visualization of the key statistical features, extracted from the data. For the key features extraction we utilize a statistical framework performing estimation, clustering with alignment of temporal omic fold changes originating from two-sample time course data. The approach was applied to real transcriptomic data obtained with two different types of irradiation. The results were analyzed using biological literature and enrichment analysis, thus validating the robustness of the proposed tools as well as achieving better understanding of the differences in the impact of the treatments in question.