You are here:
A statistical framework for applying RNA profiling to chemical hazard detection
Citation:
Kostich, M. A statistical framework for applying RNA profiling to chemical hazard detection. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, 188:49-59, (2017).
Impact/Purpose:
Highlights • Mechanism-agnostic approach for applying mRNA profiling to hazard detection. • Includes single-gene, multi-gene, single-test, multi-test methods. • Incorporates data from complementary sources. • Produces quantitative predictions for aquatic communities. • Complemented by mechanistic investigation.
Description:
Use of ‘omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of ‘omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with the single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of ‘omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package.
