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A simple method for assessing sample sizes in microarray experiments.

Robert Tibshirani1

  • 1Health Research & Policy, Stanford University, Stanford, CA 94305, USA. tibs@stanford.edu

BMC Bioinformatics
|March 4, 2006
PubMed
Summary
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This study presents a straightforward method for determining sample size in microarray experiments. It helps estimate gene-specific power and error rates, aiding researchers in planning robust studies.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Genetics

Background:

  • Microarray experiments are crucial for high-throughput gene expression analysis.
  • Determining appropriate sample size is essential for the statistical power and reliability of microarray studies.
  • Existing methods for sample size calculation can be complex or lack direct applicability to gene-specific metrics.

Purpose of the Study:

  • To introduce a simple and practical method for assessing sample size requirements in microarray experiments.
  • To provide a framework for estimating key statistical parameters relevant to gene discovery.
  • To demonstrate the adaptability of the proposed method to various experimental designs and response variables.

Main Methods:

  • The method utilizes output from permutation-based analyses, such as those from the Significance Analysis of Microarrays (SAM) package.

Related Experiment Videos

  • It involves estimating the false discovery rate (FDR) and false negative rate (FNR) for a list of genes across different sample sizes.
  • These estimates are directly interpretable as per-gene power and type I error rates.

    • Main Results:

    • The proposed method provides estimates of false discovery and false negative rates, which correspond to gene-specific power and type I error.
    • It allows researchers to evaluate the impact of different sample sizes on the detection of significant genes.
    • The approach is shown to be applicable to other response variables, including survival outcomes.

    Conclusions:

    • The presented method offers a useful tool for sample size assessment in the context of microarray experiments.
    • It facilitates informed decisions regarding the number of samples needed to achieve desired statistical power.
    • The method's flexibility extends its utility to a broader range of genomic and statistical applications.