Application of a mixture model for determining the cutoff threshold for activity in high-throughput screening

• Published in: Computational statistics & data analysis Vol. 51; no. 8; pp. 4002 - 4012
• Main Authors: Tong, Donald D.M., Buxser, Stephen, Vidmar, Thomas J.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2007; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Cutoff threshold; Distribution theory; Exact sciences and technology; Gamma distribution; General topics; High-throughput screening; Mathematics; Mixture model; Multivariate analysis; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Gamma distribution; High-throughput screening; Mixture model; Cutoff threshold; Mixed distribution; Data analysis; Statistical analysis; Gaussian distribution; Statistical data; Statistical computation; Reasoning; Distribution function; Distribution tail
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2006.12.014

Rapid throughput of assays for assessing biological activity of compounds, known as high-throughput screening (HTS), has created a need for a statistical analysis of the resulting data. Conventional methods for separating active compounds from inactive compounds based on using only a portion of the screening data to determine the cutoff threshold value are ad hoc and unsatisfactory. Taking full advantage of the entire set of screening data, we assume that the responses can be sorted into two classes; measurements associated with inactive compounds and measurements associated with active compounds. Both theoretical and practical considerations lead us to model the distribution of the measurements of inactive compounds with a Gaussian distribution. The choice is consistent with the data and our analytical experience. In our examples, active compounds inhibit enzyme and the distribution of measurements from those compounds can be characterized by a gamma or a similar one-sided long-tailed distribution. The application of this mixture of Gaussian and gamma distributions describes activity of our screening data very well. Using this model, we will show its reasoning and derivation along with describing how to optimally set the cutoff threshold value in a statistically well-defined manner. This modeling approach provides a new and useful statistical tool for HTS.