|Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure.
|Year of Publication
|Zhang, G, Marvel, S, Truong, L, Tanguay, RL, Reif, DM
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.
|PubMed Central ID
|P30 ES025128 / 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