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Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: May 26, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Power and sample size calculation for microarray studies.

Sin-Ho Jung¹, S Stanley Young

¹Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.

Journal of Biopharmaceutical Statistics

|December 30, 2011

Summary

This summary is machine-generated.

Accurate sample size calculation for microarray studies is crucial for gene discovery. This study introduces new methods to control the family-wise error rate (FWER) by accounting for gene correlations, ensuring reliable results in confirmatory experiments.

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Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

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Published on: August 10, 2009

Area of Science:

Genomics
Biostatistics
Bioinformatics

Background:

Microarray technology enables large-scale gene screening for disease research.
Controlling for multiple testing, such as the family-wise error rate (FWER), is essential for accurate gene discovery.
Gene expression data often exhibits correlations due to biological and experimental factors, complicating statistical analysis.

Purpose of the Study:

To develop accurate sample size and power calculation methods for microarray studies.
To address the challenge of accurately reflecting gene correlation structures in sample size determination.
To provide methods for designing confirmatory microarray experiments, especially when pilot data is available or a two-stage approach is needed.

Main Methods:

Proposed novel sample size and power calculation methods tailored for microarray data.
Incorporated methods to account for the complex correlation structures inherent in gene expression data.
Utilized permutation methods for accurate FWER control and validated through simulations.

Main Results:

The proposed sample size calculation methods accurately maintain statistical power.
The methods effectively control the FWER by considering gene dependencies.
Simulations demonstrated the reliability of the calculated sample sizes.

Conclusions:

Accurate sample size calculation is vital for the success of confirmatory microarray studies.
The developed methods provide a robust approach to designing experiments with complex gene correlation structures.
These methods enhance the reliability of gene discovery by ensuring adequate statistical power and controlled error rates.