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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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Affymetrix GeneChip microarray preprocessing for multivariate analyses.

Matthew N McCall¹, Anthony Almudevar

¹Department of Biostatistics and Computational Biology, University of Rochester Medical Center, NY, USA. mccallm@gmail.com

Briefings in Bioinformatics

|January 3, 2012

Summary

This summary is machine-generated.

Preprocessing methods significantly impact complex gene expression analyses. This study guides users on selecting appropriate methods for multivariate analyses like gene co-expression and network modeling using Affymetrix GeneChip data.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Affymetrix GeneChip microarrays are a prevalent high-throughput tool for gene expression measurement.
Various preprocessing methods exist to convert raw probe intensities into gene expression values.
Existing comparisons primarily focus on differential expression and clustering analyses.

Purpose of the Study:

To investigate the influence of different preprocessing methods on multivariate gene expression analyses.
To provide recommendations for optimal preprocessing strategies for advanced analyses.
To address the common practice of applying standard preprocessing to complex analyses.

Main Methods:

Evaluation of multiple preprocessing algorithms for Affymetrix GeneChip data.
Assessment of preprocessing impact on gene co-expression analysis.
Assessment of preprocessing impact on differential co-expression analysis.
Assessment of preprocessing impact on gene set analysis.
Assessment of preprocessing impact on network modeling.

Main Results:

Preprocessing methods demonstrably affect the outcomes of multivariate analyses.
Specific preprocessing techniques show superior performance for certain complex analyses.
The choice of preprocessing is critical for reliable interpretation of advanced gene expression studies.

Conclusions:

Standard preprocessing methods may not be optimal for complex multivariate analyses.
Users should carefully consider preprocessing choices based on the specific downstream analysis.
This work offers guidance for selecting appropriate preprocessing for advanced gene expression studies.