Abstract

Quality control is a key component of high-content screening for novel drug discovery endeavors and phenotype-genotype mapping. Current methods such Z-factor and strictly standardized mean difference (SSMD) rely on a normal distribution assumption with control data. Unfortunately, this assumption often times is not accurate, leading to low quality scores on viable biological assessments (bioassays). This can result in lost resources, increased cost and wasted time in attempting to optimize a bioassay. We propose a novel non-parametric approach that is robust to noise and is capable of assessing the quality of bioassays where the control data may not follow a Gaussian distribution. We demonstrate that our method produces accurate results when assessing the quality of real-world siRNA and small-molecule screens. We test the proposed quality score on synthetic data using different distributions and demonstrate that our method provides a more accurate assessment of data separation on non-Gaussian datasets as well.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
PublisherThe International Society for Computers and Their Applications (ISCA)
Pages209-216
Number of pages8
ISBN (Electronic)9781943436071
StatePublished - 2017
Event9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 - Honolulu, United States
Duration: Mar 20 2017Mar 22 2017

Other

Other9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
CountryUnited States
CityHonolulu
Period3/20/173/22/17

Fingerprint

Bioassay
Biological Assay
Assays
Screening
Normal Distribution
Quality Control
Gaussian distribution
Normal distribution
Drug Discovery
Small Interfering RNA
Quality control
Noise
Genotype
Phenotype
Costs and Cost Analysis
Molecules
Costs

Keywords

  • High content screening
  • K-S test

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Information Management

Cite this

Trevino, R. P., Faucon, P. C., Lamkin, T. J., Kawamoto, S. A., Smith, R., & Liu, H. (2017). Non-parametric quality assessment of high-content screening assays. In Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 (pp. 209-216). The International Society for Computers and Their Applications (ISCA).

Non-parametric quality assessment of high-content screening assays. / Trevino, Robert P.; Faucon, Philippe C.; Lamkin, Thomas J.; Kawamoto, Steven A.; Smith, Ross; Liu, Huan.

Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA), 2017. p. 209-216.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Trevino, RP, Faucon, PC, Lamkin, TJ, Kawamoto, SA, Smith, R & Liu, H 2017, Non-parametric quality assessment of high-content screening assays. in Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA), pp. 209-216, 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017, Honolulu, United States, 3/20/17.
Trevino RP, Faucon PC, Lamkin TJ, Kawamoto SA, Smith R, Liu H. Non-parametric quality assessment of high-content screening assays. In Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA). 2017. p. 209-216
Trevino, Robert P. ; Faucon, Philippe C. ; Lamkin, Thomas J. ; Kawamoto, Steven A. ; Smith, Ross ; Liu, Huan. / Non-parametric quality assessment of high-content screening assays. Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA), 2017. pp. 209-216
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