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Reprod Toxicol. 2016 Jul;62:92-9. doi: 10.1016/j.reprotox.2016.04.012. Epub 2016 Apr 27.

Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure.

Author information

  • 1Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. Electronic address: gzhang6@ncsu.edu.
  • 2Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. Electronic address: swmarvel@ncsu.edu.
  • 3Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR, USA. Electronic address: lisa.truong@oregonstate.edu.
  • 4Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR, USA. Electronic address: robert.tanguay@oregonstate.edu.
  • 5Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA; Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA. Electronic address: dmreif@ncsu.edu.

Abstract

Robust computational approaches are needed to characterize systems-level responses to chemical perturbations in environmental and clinical toxicology applications. Appropriate characterization of response presents a methodological challenge when dealing with diverse phenotypic endpoints measured using in vivo systems. In this article, we propose an information-theoretic method named Aggregate Entropy (AggE) and apply it to scoring multiplexed, phenotypic endpoints measured in developing zebrafish (Danio rerio) across a broad concentration-response profile for a diverse set of 1060 chemicals. AggE accurately identified chemicals with significant morphological effects, including single-endpoint effects and multi-endpoint responses that would have been missed by univariate methods, while avoiding putative false-positives that confound traditional methods due to irregular correlation structure. By testing AggE in a variety of high-dimensional real and simulated datasets, we have characterized its performance and suggested implementation parameters that can guide its application across a wide range of experimental scenarios.

KEYWORDS:

Chemical biology; Developmental neurotoxicology; High throughput screening; Morphology; Multiplexed assays; ToxCast; Zebrafish

PMID:
27132190
PMCID:
PMC4905797
[Available on 2017-07-01]
DOI:
10.1016/j.reprotox.2016.04.012
[PubMed - in process]
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