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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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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.
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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Power and sample size estimation in microarray studies.

Wei-Jiun Lin¹, Huey-Miin Hsueh, James J Chen

¹Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA.

BMC Bioinformatics

|January 27, 2010

Summary

This summary is machine-generated.

Determining the correct sample size for microarray experiments is crucial for identifying differentially expressed genes. A new permutation method ensures desired sensitivity with 95% probability, outperforming traditional approaches.

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Area of Science:

Genomics
Biostatistics
Bioinformatics

Background:

Accurate sample size estimation is critical for microarray experiments to ensure sufficient statistical power for identifying differentially expressed genes.
Common sample size estimation methods may yield inadequate power, with a low probability of detecting true biological signals.

Purpose of the Study:

To address the limitations of existing methods by reformulating the sample size problem.
To propose a novel permutation-based method for sample size estimation in gene expression studies.

Main Methods:

The study reformulates sample size estimation as achieving a specified sensitivity with 95% probability.
A permutation method is proposed, utilizing a small pilot dataset to estimate the required number of arrays.
This method accounts for gene correlation and effect size heterogeneity.

Main Results:

Traditional sample size calculations may result in a probability of detecting the specified sensitivity below 50%.
The proposed permutation method guarantees the desired sensitivity detection with 95% probability.
The method demonstrates effectiveness with pilot datasets as small as 4-6 samples per group.

Conclusions:

Sample size estimation should be formulated to guarantee detection of differentially expressed genes with high probability (95%).
The proposed permutation method provides a robust approach by considering gene correlations and effect size variations.
This method is practical, requiring only a small pilot dataset for reliable sample size determination.