Title | A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Zhang, G, Roell, KR, Truong, L, Tanguay, RL, Reif, DM |
Journal | Toxicol Appl Pharmacol |
Volume | 314 |
Pagination | 109-117 |
Date Published | 2017 Jan 01 |
ISSN | 1096-0333 |
Keywords | Animals, Models, Theoretical, Multivariate Analysis, Phenotype, Zebrafish |
Abstract | Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. |
DOI | 10.1016/j.taap.2016.11.010 |
Alternate Journal | Toxicol. Appl. Pharmacol. |
PubMed ID | 27884602 |
PubMed Central ID | PMC5224523 |
Grant List | P30 ES025128 / ES / NIEHS NIH HHS / United States U01 ES027294 / ES / NIEHS NIH HHS / United States R01 ES023788 / ES / NIEHS NIH HHS / United States T32 ES007329 / ES / NIEHS NIH HHS / United States R01 ES019604 / ES / NIEHS NIH HHS / United States RC4 ES019764 / ES / NIEHS NIH HHS / United States P42 ES016465 / ES / NIEHS NIH HHS / United States P30 ES000210 / ES / NIEHS NIH HHS / United States P42 ES005948 / ES / NIEHS NIH HHS / United States |