Quality Control of High Throughput Screening


[Master of Science by Research thesis]. High Throughput Screening (HTS) is an efficient way of assessing the biological activity of a large number of compounds in order to determine the few compounds that could lead to the development of a pharmaceutical product of commercial value. The process consists of screening a large number of mixtures using the standard 96-well plate featuring amongst others six specific control wells whose expected value is known. Because the measurement technique is subject to variation and because of the large number of plates involved, the quality assessment of the data is difficult and therefore automation appears to be a necessity. We propose a three-step procedure for the quality control of the data. It first consists of a study based on control wells, where a Gaussian mixture, trained with the EM algorithm, models the distribution of the control values to determine general variations on a whole screen together with any errors that would affect the control wells. The second step relies on normal wells and is based on a plate to plate comparison and an intra-plate variation detection that aims at spotting general effects such as handling mistakes or blocked jets. The Kolmogorov-Smirnov procedure was chosen to perform inter-plate comparisons whereas Siegel-Tukey and Wilcoxon tests investigate differences in spread and location in the data within a plate. The Analysis of Variance techniques complete the quality control of the screening process by focusing on the detection of systematic edge and corner effects.

Publication DOI: https://doi.org/10.48780/publications.aston.ac.uk.00021623
Additional Information: Copyright © Hervé Christian Zilliox, 1998. Hervé Christian Zilliox asserts their moral right to be identified as the author of this thesis. This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without appropriate permission or acknowledgement. If you have discovered material in Aston Publications Explorer which is unlawful e.g. breaches copyright, (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please read our Takedown Policy and contact the service immediately.
Institution: Aston University
Uncontrolled Keywords: Gaussian Mixture Model,EM algorithm,Statistical tests,Analysis of Variance,High Throughput Screening,Machine Learning,Applied Mathematics,Neural Networks
Last Modified: 20 Dec 2023 16:50
Date Deposited: 19 Mar 2014 16:10
Completed Date: 1998-09
Authors: Zilliox, Hervé C.

Export / Share Citation


Additional statistics for this record