UM  > Faculty of Health Sciences
Affiliated with RCfalse
Robust statistical methods for hit selection RNA interference high-throughput screening experiments
Zhang X.D.1; Yang X.C.2; Chung N.1; Gates A.1; Stec E.1; Kunapuli P.1; Holder D.J.1; Ferner M.1; Espeseth A.S.1
Source PublicationPharmacogenomics
ISSN14622416 17448042
AbstractRNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean ± k standard deviation (SD) and median ± 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean ± k SD under the same preset error rate. The number of hits selected by median ± k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (determining effective siRNAs on a plate-by-plate basis) can adjust for systematic errors in different plates, while an experimentwise analysis, in which effective siRNAs are identified in an analysis of the entire experiment, cannot. However, experimentwise analysis may detect a cluster of true positive hits placed together in one or several plates, while platewise analysis may not. To display hit selection results, we designed a specific figure called a plate-well series plot. We thus suggest the following strategy for hit selection in RNAi HTS experiments. First, choose the quartile-based method, or median ± k MAD, for identifying effective siRNAs. Second, perform the chosen method experimentwise on transformed/normalized data, such as percentage inhibition, to check the possibility of hit clusters. If a cluster of selected hits are observed, repeat the analysis based on untransformed data to determine whether the cluster is due to an artifact in the data. If no clusters of hits are observed, select hits by performing platewise analysis on transformed data. Third, adopt the plate-well series plot to visualize both the data and the hit selection results, as well as to check for artifact. © 2006 Future Medicine Ltd.
KeywordExperimentwise High-throughput screening Hit selection Median Median absolute deviation Plate-well series plot Platewise Quartile-based method RNA interference Statistical methods
URLView the original
Fulltext Access
Citation statistics
Cited Times [WOS]:74   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
Affiliation1.Merck Research Laboratories
2.Carnegie Mellon University
Recommended Citation
GB/T 7714
Zhang X.D.,Yang X.C.,Chung N.,et al. Robust statistical methods for hit selection RNA interference high-throughput screening experiments[J]. Pharmacogenomics,2006,7(3):299-309.
APA Zhang X.D.,Yang X.C.,Chung N.,Gates A.,Stec E.,Kunapuli P.,Holder D.J.,Ferner M.,&Espeseth A.S..(2006).Robust statistical methods for hit selection RNA interference high-throughput screening experiments.Pharmacogenomics,7(3),299-309.
MLA Zhang X.D.,et al."Robust statistical methods for hit selection RNA interference high-throughput screening experiments".Pharmacogenomics 7.3(2006):299-309.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang X.D.]'s Articles
[Yang X.C.]'s Articles
[Chung N.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang X.D.]'s Articles
[Yang X.C.]'s Articles
[Chung N.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang X.D.]'s Articles
[Yang X.C.]'s Articles
[Chung N.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.