A note on statistical repeatability and study design for high-throughput assays.
Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose p...
| Published in: | Statistics in Medicine Vol. 36; no. 5; pp. 790 - 799 |
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| Main Authors: | , |
| Format: | equations & formulas research tables/charts Journal Article |
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Wiley-Blackwell
2/28/2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120746819&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120746819 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 2/28/2017 vid: 36 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 120746819 120746819 NLM27882571 120746819 10.1002/sim.7175 NLM27882571 120746819 ppf: 790 ppct: 9 formats: tig: atl: A note on statistical repeatability and study design for high-throughput assays. aug: au: Nicholson, George Holmes, Chris affil: Department of Statistics, University of Oxford, 24‐29 St Giles, Oxford OX1 3LB, U.K. sug: subj: Study Design Statistics Methods Reproducibility of Results High-Throughput Screening Assays Methods High-Throughput Screening Assays Standards Data Analysis, Statistical Sample Size Funding Source Human ab: Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms. We demonstrate that such scatterplots convey little statistical information of relevance and are potentially highly misleading. We present an alternative framework for assessing the precision of high-throughput assays and planning biomedical experiments. Our methods are based on repeatability-a long-established statistical quantity also known as the intraclass correlation coefficient. We provide guidance and software for estimation and visualization of repeatability of high-throughput assays, and for its incorporation into study design. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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