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A note on statistical repeatability and study design for high-throughput assays.

George Nicholson1, Chris Holmes1

  • 1Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, U.K.

Statistics in Medicine
|November 25, 2016
PubMed
Summary
This summary is machine-generated.

Scatterplots for assessing high-throughput assay precision are misleading. A new framework using repeatability (intraclass correlation coefficient) offers a statistically sound alternative for experimental design and sample size calculation in biomedical research.

Keywords:
high-throughput assayrepeatabilityscatterplotstudy designtechnical replicate

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Area of Science:

  • Biomedical Research
  • Genomics
  • Statistical Methods

Background:

  • Assessing measurement precision is crucial for experimental design and sample size calculation.
  • High-throughput assays in biomedical research present unique statistical challenges.
  • Current methods often rely on scatterplots of technical replicates, which may be inadequate.

Purpose of the Study:

  • To question the statistical validity of scatterplots for assessing high-throughput assay precision.
  • To propose an alternative statistical framework for precision assessment and experimental planning.
  • To provide practical tools for implementing the proposed methods.

Main Methods:

  • Empirical and theoretical analysis of scatterplot methods.
  • Development of a framework based on repeatability (intraclass correlation coefficient).
  • Illustration with four genomic data sets.

Main Results:

  • Scatterplots of technical replicates provide limited and potentially misleading statistical information.
  • The proposed framework based on repeatability offers a more robust approach.
  • Guidance and software are provided for practical application.

Conclusions:

  • The traditional scatterplot method for assessing high-throughput assay precision is statistically questionable.
  • Repeatability, quantified by the intraclass correlation coefficient, provides a reliable measure for precision assessment.
  • The new framework enhances the planning of biomedical experiments and sample size calculations.