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Relative power and sample size analysis on gene expression profiling data.

M van Iterson1, P A C 't Hoen, P Pedotti

  • 1Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.

BMC Genomics
|September 18, 2009
PubMed
Summary
This summary is machine-generated.

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Choosing the right gene expression profiling technology is crucial for experimental design. Power and sample size analysis using pilot data helps select optimal methods, with deep sequencing excelling for low-intensity signals.

Area of Science:

  • Genomics
  • Bioinformatics
  • Experimental Design

Background:

  • Selecting appropriate gene expression profiling technologies is challenging due to varying data variability and sample types.
  • Objective measures for experimental design, utilizing pilot data, are essential for optimizing resource allocation.

Purpose of the Study:

  • To perform relative power and sample size analysis on distinct gene expression datasets.
  • To compare the performance of different expression profiling technologies, including microarrays and deep sequencing.
  • To provide guidance for experimental design based on pilot data analysis.

Main Methods:

  • Analysis of Affymetrix array data from a nutrigenomics experiment.
  • Comparison of microarray platforms (Agilent, Affymetrix) with Solexa/Illumina deep sequencing.

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  • Simulation experiments to predict relative power based on observed effect sizes.

    • Main Results:

    • Larger effect sizes positively correlate with increased experimental power.
    • Solexa/Illumina deep sequencing demonstrates higher overall power compared to microarray platforms.
    • Deep sequencing maintains consistent power across intensity ranges, unlike microarrays which decline at low intensities.

    Conclusions:

    • Power and sample size analysis using pilot data significantly inform experimental design decisions.
    • Solexa/Illumina deep sequencing is recommended for detecting genes with low expression levels.
    • A BioConductor package, SSPA, is available to guide researchers in experimental design using their own pilot data.