Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of quantitative high throughput screening data using a robust method for nonlinear mixed effects models

  • Park, Chorong (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Jongga (Department of Applied Statistics, Chung-Ang University) ;
  • Lim, Changwon (Department of Applied Statistics, Chung-Ang University)
  • Received : 2020.09.29
  • Accepted : 2020.11.01
  • Published : 2020.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

Keywords

References

  1. Filer DL, Kothiya P, Setzer RW, Judson RS, and Martin MT (2017). tcpl: the ToxCast pipeline for high-throughput screening data, Bioinformatics, 33, 618-620.
  2. Gill PS (2000). A robust mixed linear model analysis for longitudinal data, Statistics in Medicine, 19, 975-987. https://doi.org/10.1002/(SICI)1097-0258(20000415)19:7<975::AID-SIM381>3.0.CO;2-9
  3. Huang R, Sakamuru S, Martin MT, et al. (2014). Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway, Scientific Reports, 4, 5664. https://doi.org/10.1038/srep05664
  4. Huber PJ and Ronchetti EM (2009). Robust Statistics (2nd Ed), John Wiley & Sons, New York.
  5. Inglese J, Auld DS, Jadhav A, Johnson RL, Simeonov A, Yasgar A, Zheng W, and Austin CP (2006). Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries, Proceedings of the National Academy of Sciences of the United States of America, 103, 11473-11478. https://doi.org/10.1073/pnas.0604348103
  6. Lim C (2015). Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data, Statistics in Medicine, 34, 1185-1198. https://doi.org/10.1002/sim.6391
  7. Lim C, Sen PK, and Peddada SD (2012). Accounting for uncertainty in heteroscedasticity in nonlinear regression, Journal of Statistical Planning and Inference, 142, 1047-1062. https://doi.org/10.1016/j.jspi.2011.11.003
  8. Lim C, Sen PK, and Peddada SD (2013a). Robust analysis of high throughput screening (HTS) assay data, Technometrics, 55, 150-160. https://doi.org/10.1080/00401706.2012.749166
  9. Lim C, Sen PK, and Peddada SD (2013b). Robust nonlinear regression in applications, Journal of the Indian Society of Agricultural Statistics 67, 215-234.
  10. Lindstrom ML and Bates DM (1990). Nonlinear mixed effects models for repeated measures data, Biometrics, 46, 673-687. https://doi.org/10.2307/2532087
  11. Mancini L, Ronchetti E, and Trojani F (2005). Optimal conditionally unbiased bounded-influence inference in dynamic location and scale models, Journal of the American Statistical Association, 100, 628-641. https://doi.org/10.1198/016214504000001402
  12. Meza C, Osorio F, and Cruz RD (2012). Estimation in nonlinear mixed-effects models using heavy-tailed distributions, Statistics and Computing, 22, 121-139. https://doi.org/10.1007/s11222-010-9212-1
  13. Michael S, Auld D, Klumpp C, Jadhav A, Zheng W, Thorne N, Austin CP, Inglese J, and Simeonov A (2008). A robotic platform for quantitative high-throughput screening, ASSAY and Drug Development Technologies, 6, 637-657. https://doi.org/10.1089/adt.2008.150
  14. Parham F, Austin C, Southall N, Huang R, Tice R, and Portier C (2009). Dose-response modeling of high-throughput screening data, Journal of Biomolecular Screening, 14, 1216-1227. https://doi.org/10.1177/1087057109349355
  15. Shockley KR (2012). A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data, Environmental Health Perspectives, 120, 1107-1115. https://doi.org/10.1289/ehp.1104688
  16. Shockley KR (2015). Quantitative high-throughput screening data analysis: challenges and recent advances, Drug Discovery Today, 20, 296-300. https://doi.org/10.1016/j.drudis.2014.10.005
  17. Shockley K, Gupta S, Harris S, Lahiri SN, and Peddada S (2019). Quality control of quantitative high throughput screening data, Frontiers in Genetics, 10, 387. https://doi.org/10.3389/fgene.2019.00387
  18. Strathe AB, Danfaer A, Sorensen H, and Kebreab E (2010). A multilevel nonlinear mixed-effects approach to model growth in pigs, Journal of Animal Science, 88, 638-649. https://doi.org/10.2527/jas.2009-1822
  19. Thomas RS, Black MB, Li L, Healy E, Chu TM, Bao W, Andersen MD, and Wolfinger RD (2012). A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening, Toxicological Sciences 128, 398-417. https://doi.org/10.1093/toxsci/kfs159
  20. Williams JD, Birch JB, and Abdel-Salam AS (2015). Outlier robust nonlinear mixed model estimation, Statistics in Medicine, 34, 1304-1316. https://doi.org/10.1002/sim.6406
  21. Xia M, Huang R, Witt KL, et al. (2008). Compound cytotoxicity profiling using quantitative high-throughput screening, Environmental Health Perspectives, 116, 284-291. https://doi.org/10.1289/ehp.10727
  22. Yeap BY and Davidian M (2001). Robust two-stage estimation in hierarchical nonlinear models, Biometrics, 57, 266-272. https://doi.org/10.1111/j.0006-341X.2001.00266.x