John Wiley & Sons, Ltd.

High throughput concentration‐response analysis for omics datasets

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Omics‐based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms is available and may be utilized for identifying modes of action, adverse outcome pathways or novel biomarkers. For all these purposes good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration‐response modelling is rarely used for large datasets. Instead statistical hypothesis testing is prevalent, which provides only limited scope for inference. This study therefore applied automated concentration‐response modelling for three different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modelling process was performed by simultaneously applying nine different regression models, representing distinct mechanistic, toxicological and statistical ideas that result in different curve shapes. The best fitting models were selected using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50 % of responses. Models generating U‐shaped curves were frequently selected for transcriptomic signals (30 %), and sigmoid models were identified as best fit for many metabolomic signals (21 %). Thus, selecting the models from an array of different types seems appropriate, since concentration‐response functions may vary due to the observed response type and also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentrationresponse models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level. This article is protected by copyright. All rights reserved

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