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

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

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Embryo, Nonmammalian
  • Entropy
  • Flame Retardants / toxicity
  • High-Throughput Screening Assays*
  • Models, Theoretical*
  • Phenotype
  • Teratogens / toxicity*
  • Zebrafish / embryology*

Substances

  • Flame Retardants
  • Teratogens