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Emerging Bioinformatic Tools in Toxicogenomics

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00387

Quality Control of Quantitative High Throughput Screening Data

  • 1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences (NIEHS), United States
  • 2Statistics, University of California, Davis, United States
  • 3Social and Scientific Systems (United States), United States
  • 4Statistics, North Carolina State University, United States
  • 5University of Pittsburgh, United States

Quantitative high throughput screening (qHTS) experiments can generate thousands of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and precision of concentration at half-maximal response (AC50) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with “inconsistent” response patterns and produces trustworthy AC50 values.

Keywords: anova, clustering, Concentration-response, Potency, quantitative high throughput screening, Toxicological response

Received: 24 May 2018; Accepted: 10 Apr 2019.

Edited by:

Danyel Jennen, Department of Toxicogenomics, Maastricht University, Netherlands

Reviewed by:

Matthew T. Martin, Pfizer (United States), United States
Katie Paul Friedman, National Center for Computational Toxicology (NCCT), United States  

Copyright: © 2019 Shockley, Gupta, Harris, Lahiri and Peddada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Shyamal Peddada, University of Pittsburgh, Pittsburgh, United States, sdp47@pitt.edu