|
Sign In to gain access to subscriptions and/or personal tools.
|
Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data
Bert Gunter
Biometrics Research Department, Merck Research Laboratories, Rahway, NJbert_gunter{at}merck.com
Christine Brideau
Department of Biochemistry and Molecular Biology, Merck Frosst Centre for Therapeutic Research, Kirkland, Quebec, Canada
Bill Pikounis
Andy Liaw
Biometrics Research Department, Merck Research Laboratories, Rahway, NJ
High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server® HTS application, a custom-developed software tool built from the commercially available S-PLUS® and Stat Server® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.
Key Words: HTS • quality control • robust fitting • trend analysis
Journal of Biomolecular Screening, Vol. 8, No. 6,
624-633 (2003)
DOI: 10.1177/1087057103258284
 CiteULike  Complore  Connotea  Del.icio.us  Digg  Reddit  Technorati  Twitter What's this?
This article has been cited by other articles:
|
|
|
|
 
Y. Eberhard, S. P. McDermott, X. Wang, M. Gronda, A. Venugopal, T. E. Wood, R. Hurren, A. Datti, R. A. Batey, J. Wrana, et al.
Chelation of intracellular iron with the antifungal agent ciclopirox olamine induces cell death in leukemia and myeloma cells
Blood,
October 1, 2009;
114(14):
3064 - 3073.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
I. Coma, L. Clark, E. Diez, G. Harper, J. Herranz, G. Hofmann, M. Lennon, N. Richmond, M. Valmaseda, and R. Macarron
Process Validation and Screen Reproducibility in High-Throughput Screening
J Biomol Screen,
January 1, 2009;
14(1):
66 - 76.
[Abstract]
[PDF]
|
|
|
|
|
|
|
X. D. Zhang, P. F. Kuan, M. Ferrer, X. Shu, Y. C. Liu, A. T. Gates, P. Kunapuli, E. M. Stec, M. Xu, S. D. Marine, et al.
Hit selection with false discovery rate control in genome-scale RNAi screens
Nucleic Acids Res.,
August 1, 2008;
36(14):
4667 - 4679.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. D. Zhang, A. S. Espeseth, E. N. Johnson, J. Chin, A. Gates, L. J. Mitnaul, S. D. Marine, J. Tian, E. M. Stec, P. Kunapuli, et al.
Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens
J Biomol Screen,
June 1, 2008;
13(5):
378 - 389.
[Abstract]
[PDF]
|
|
|
|
|
|
|
X. D. Zhang
Novel Analytic Criteria and Effective Plate Designs for Quality Control in Genome-Scale RNAi Screens
J Biomol Screen,
June 1, 2008;
13(5):
363 - 377.
[Abstract]
[PDF]
|
|
|
|
|
|
|
N. Chung, X. D. Zhang, A. Kreamer, L. Locco, P.-F. Kuan, S. Bartz, P. S. Linsley, M. Ferrer, and B. Strulovici
Median Absolute Deviation to Improve Hit Selection for Genome-Scale RNAi Screens
J Biomol Screen,
February 1, 2008;
13(2):
149 - 158.
[Abstract]
[PDF]
|
|
|
|
|
|
|
K. P. Seiler, G. A. George, M. P. Happ, N. E. Bodycombe, H. A. Carrinski, S. Norton, S. Brudz, J. P. Sullivan, J. Muhlich, M. Serrano, et al.
ChemBank: a small-molecule screening and cheminformatics resource database
Nucleic Acids Res.,
January 11, 2008;
36(suppl_1):
D351 - D359.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. D. Zhang
A New Method with Flexible and Balanced Control of False Negatives and False Positives for Hit Selection in RNA Interference High-Throughput Screening Assays
J Biomol Screen,
August 1, 2007;
12(5):
645 - 655.
[Abstract]
[PDF]
|
|
|
|
|
|
|
V. Makarenkov, P. Zentilli, D. Kevorkov, A. Gagarin, N. Malo, and R. Nadon
An efficient method for the detection and elimination of systematic error in high-throughput screening
Bioinformatics,
July 1, 2007;
23(13):
1648 - 1657.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. D. Zhang, M. Ferrer, A. S. Espeseth, S. D. Marine, E. M. Stec, M. A. Crackower, D. J. Holder, J. F. Heyse, and B. Strulovici
The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments
J Biomol Screen,
June 1, 2007;
12(4):
497 - 509.
[Abstract]
[PDF]
|
|
|
|
|
|
|
Y. Sui and Z. Wu
Alternative Statistical Parameter for High-Throughput Screening Assay Quality Assessment
J Biomol Screen,
March 1, 2007;
12(2):
229 - 234.
[Abstract]
[PDF]
|
|
|
|
|
|
|
A. Gagarin, V. Makarenkov, and P. Zentilli
Using Clustering Techniques to Improve Hit Selection in High-Throughput Screening
J Biomol Screen,
December 1, 2006;
11(8):
903 - 914.
[Abstract]
[PDF]
|
|
|
|
|
|
|
V. Makarenkov, D. Kevorkov, P. Zentilli, A. Gagarin, N. Malo, and R. Nadon
HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data
Bioinformatics,
June 1, 2006;
22(11):
1408 - 1409.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Kevorkov and V. Makarenkov
Statistical Analysis of Systematic Errors in High-Throughput Screening
J Biomol Screen,
September 1, 2005;
10(6):
557 - 567.
[Abstract]
[PDF]
|
|
|
|
|
|
|
P. Gribbon, R. Lyons, P. Laflin, J. Bradley, C. Chambers, B. S. Williams, W. Keighley, and A. Sewing
Evaluating Real-Life High-Throughput Screening Data
J Biomol Screen,
March 1, 2005;
10(2):
99 - 107.
[Abstract]
[PDF]
|
|
|
|
|
|
|
C. Brideau, B. Gunter, B. Pikounis, and A. Liaw
Improved Statistical Methods for Hit Selection in High-Throughput Screening
J Biomol Screen,
December 1, 2003;
8(6):
634 - 647.
[Abstract]
[PDF]
|
|
|
|
|
