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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 |
| Publisher | The International Society for Computers and Their Applications (ISCA) |
| Pages | 209-216 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781943436071 |
| State | Published - 2017 |
| Event | 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 - Honolulu, United States Duration: Mar 20 2017 → Mar 22 2017 |
Other
| Other | 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 3/20/17 → 3/22/17 |
Fingerprint
Keywords
- High content screening
- K-S test
ASJC Scopus subject areas
- Biomedical Engineering
- Health Information Management
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Non-parametric quality assessment of high-content screening assays
AU - Trevino, Robert P.
AU - Faucon, Philippe C.
AU - Lamkin, Thomas J.
AU - Kawamoto, Steven A.
AU - Smith, Ross
AU - Liu, Huan
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - High content screening
KW - K-S test
UR - http://www.scopus.com/inward/record.url?scp=85016564837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016564837&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85016564837
SP - 209
EP - 216
BT - Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
PB - The International Society for Computers and Their Applications (ISCA)
ER -