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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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Comments on the analysis of unbalanced microarray data.

Kathleen F Kerr¹

¹Department of Biostatistics, Box 357232, University of Washington, Seattle, WA 98195, USA. katiek@u.washington.edu

Bioinformatics (Oxford, England)

|June 17, 2009

Summary

This summary is machine-generated.

Permutation testing for microarray data is unreliable with unequal sample sizes. This can lead to biased P-values and inaccurate false discovery rate (FDR) estimations, impacting the identification of differentially expressed (DE) genes.

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Permutation testing is widely used for identifying differentially expressed (DE) genes in microarray analysis.
Estimating false discovery rates (FDRs) is a common method to manage multiple testing issues.
Combining these methods can be problematic, especially with unequal sample sizes.

Purpose of the Study:

To investigate the suitability and potential biases of permutation testing when applied to microarray data with unequal sample sizes.
To evaluate the impact of biased P-values on FDR estimation in such scenarios.
To assess the validity of pooling permutation null distributions across genes.

Main Methods:

Analysis of microarray data using permutation testing under conditions of unequal sample sizes.
Evaluation of P-value validity and bias.
Assessment of FDR estimation accuracy using biased P-values.
Examination of pooled permutation null distributions.

Main Results:

Permutation tests may not accurately test the intended hypothesis with unbalanced data, leading to biased P-values.
Biased P-values can result in unacceptable bias in FDR estimates.
Pooling permutation null distributions across genes can generate invalid P-values, as non-DE genes may exhibit different distributions.
The effectiveness of P-values as a metric for discriminating DE genes can be compromised.

Conclusions:

Permutation testing is not recommended for microarray data analysis with unequal sample sizes due to potential biases and invalid P-values.
Researchers should exercise caution when using P-values derived from such methods for FDR estimation and gene discrimination.
Alternative statistical approaches that reliably discriminate DE genes are encouraged, with careful consideration of the associated P-value validity.