Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ University of Califo...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Statistics in Medicine
Article . 2012 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Analysis of high‐throughput screening assays using cluster enrichment

Authors: Pu, Minya; Hayashi, Tomoko; Cottam, Howard; Mulvaney, Joseph; Arkin, Michelle; Corr, Maripat; Carson, Dennis; +1 Authors

Analysis of high‐throughput screening assays using cluster enrichment

Abstract

In this paper, we describe the implementation and evaluation of a cluster‐based enrichment strategy to call hits from a high‐throughput screen using a typical cell‐based assay of 160,000 chemical compounds. Our focus is on statistical properties of the prospective design choices throughout the analysis, including how to choose the number of clusters for optimal power, the choice of test statistic, the significance thresholds for clusters and the activity threshold for candidate hits, how to rank selected hits for carry‐forward to the confirmation screen, and how to identify confirmed hits in a data‐driven manner. Whereas previously the literature has focused on choice of test statistic or chemical descriptors, our studies suggest that cluster size is the more important design choice. We recommend clusters to be ranked by enrichment odds ratio, not by p‐value. Our conceptually simple test statistic is seen to identify the same set of hits as more complex scoring methods proposed in the literature do. We prospectively confirm that such a cluster‐based approach can outperform the naive top X approach and estimate that we improved confirmation rates by about 31.5% from 813 using the top X approach to 1187 using our cluster‐based method. Copyright © 2012 John Wiley & Sons, Ltd.

Keywords

top X, Data Interpretation, Statistics & Probability, Statistics, Antineoplastic Agents, Statistical, hit selection, high-throughput screening, High-Throughput Screening Assays, HTS hit selection, Murcko fragments, Research Design, Data Interpretation, Statistical, Drug Discovery, Public Health and Health Services, Odds Ratio, Cluster Analysis, Humans, False Positive Reactions, Prospective Studies, fingerprint descriptors, cluster analysis

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Average
Average
Green
bronze