Supporting data for "TinderMIX: Time-Dose INtegrated moDelling of toxicogenomics data"

Omics technologies have been widely applied in toxicology studies in order to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow studying molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis is not able to identify dynamic (time-depended) events of dose responsiveness.
We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log-fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify if the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure (POD).
To showcase the TinderMIX method, we analyzed two drugs from the Open TG-GATEs dataset, namely cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action (MOA) of each drug and compared them. Our analysis highlights that different time and dose integrated POD recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent MOA.