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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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Updated: Jun 26, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Filtering for increased power for microarray data analysis.

Amber J Hackstadt¹, Ann M Hess

¹Center for Bioinformatics and Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA. hackstad@stat.colostate.edu

BMC Bioinformatics

|January 10, 2009

Summary

This summary is machine-generated.

Filtering microarray data before analysis increases the power to detect differentially expressed genes. Variance and detection call filtering methods improve gene identification while controlling the false discovery rate.

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Published on: August 10, 2009

Area of Science:

Genomics
Bioinformatics
Statistical Analysis

Background:

Multiple hypothesis testing in microarray analysis necessitates adjustments.
High test numbers and low differential expression proportions reduce detection power.
Filtering reduces tests, enhancing power for identifying differentially expressed genes.

Purpose of the Study:

To evaluate filtering methods combined with false discovery rate control.
To assess the impact of filtering on the power of differential gene expression analysis.

Main Methods:

Compared variance, average signal, and MAS detection call filtering.
Utilized Benjamini-Hochberg for false discovery rate control.
Employed q-value for false discovery rate estimation.
Conducted case studies and simulation studies.

Main Results:

Detection call and variance filtering increased identified differentially expressed genes.
Variance filtering on the log2 scale negatively impacted results with specific preprocessing methods.
Simulation confirmed variance filtering enhances power and controls false discovery rate.

Conclusions:

Detection call and variance filtering are effective for identifying differentially expressed genes.
Variance filtering boosts power while maintaining false discovery rate control.
Filtering approximately 50% of probe sets is reasonable when differential expression is low.