Bayesian Multi-Plate High-Throughput Screening of Compounds

Sci Rep. 2018 Jun 22;8(1):9551. doi: 10.1038/s41598-018-27531-w.


High-throughput screening of compounds (chemicals) is an essential part of drug discovery, involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross-plate correlation or statistical strength among plates. We present a new statistical framework for high-throughput screening of compounds based on Bayesian nonparametric modeling. The proposed approach is able to identify candidate hits from multiple plates simultaneously, sharing statistical strength among plates and providing more robust estimates of compound activity. It can flexibly accommodate arbitrary distributions of compound activities and is applicable to any plate geometry. The algorithm provides a principled statistical approach for hit identification and false discovery rate control. Experiments demonstrate significant improvements in hit identification sensitivity and specificity over the B-score and R-score methods, which are highly sensitive to threshold choice. These improvements are maintained at low hit rates. The framework is implemented as an efficient R extension package BHTSpack and is suitable for large scale data sets.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bayes Theorem
  • Cell Division / drug effects
  • DNA Damage
  • Drug Evaluation, Preclinical / methods*
  • Escherichia coli / cytology
  • Escherichia coli / drug effects
  • Escherichia coli / genetics
  • High-Throughput Screening Assays / methods*