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Related Concept Videos

One-Way ANOVA: Equal Sample Sizes01:15

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pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray

Qing Xia1, Jeffrey A Thompson1, Devin C Koestler1

  • 1Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA.

Statistical Applications in Genetics and Molecular Biology
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Batch effect Reduction of microarray data with Dependent samples usinG Empirical Bayes (BRIDGE) corrects batch effects in studies with dependent samples. The new pwrBRIDGE tool estimates the optimal number of bridging samples for accurate statistical power in microarray analysis.

Keywords:
batch effect correctionlongitudinal gene expressionpower and sample size calculationshinyRtemporal microarray data

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Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • Batch effects are a common issue in microarray studies, confounding results.
  • Dependent samples in longitudinal studies complicate batch effect correction.
  • Existing methods lack a framework for optimizing sample size for statistical power.

Purpose of the Study:

  • To develop a statistical framework and user-friendly software for estimating the required number of bridging samples (M).
  • To ensure adequate statistical power for hypothesis testing in batch-confounded microarray studies with dependent samples.
  • To aid researchers in designing robust microarray studies, preventing under- or over-powered analyses.

Main Methods:

  • Developed pwrBRIDGE, a simulation-based approach to estimate bridging sample size (M).
  • Utilizes the BRIDGE methodology, which leverages technical replicates across batches ('bridging samples').
  • Applied to a hypothetical longitudinal study tracking Alzheimer's disease progression.

Main Results:

  • pwrBRIDGE provides a method to estimate M for achieving desired statistical power.
  • The tool facilitates informed study design for batch-confounded longitudinal microarray experiments.
  • Addresses the need for a formal statistical framework for sample size estimation in BRIDGE.

Conclusions:

  • pwrBRIDGE enables researchers to determine the optimal number of bridging samples for microarray studies.
  • This tool is crucial for designing powerful and reliable studies involving dependent samples and batch effects.
  • Facilitates accurate gene identification in disease progression studies, such as Alzheimer's disease.