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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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Related Experiment Video

Updated: Jun 8, 2026

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Microarray data analysis.

Saroj K Mohapatra¹, Arjun Krishnan

¹Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|October 9, 2010

Summary

This summary is machine-generated.

This guide explains how to analyze Affymetrix microarray data using R and Bioconductor. Learn essential steps for gene expression profiling in functional genomics research.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Gene expression profiling is crucial for understanding cellular responses and developmental processes.
Microarrays are a widely adopted technology for capturing genome-wide transcriptional data.

Purpose of the Study:

To provide a comprehensive guide for analyzing Affymetrix microarray data.
To demonstrate the use of open-source R and Bioconductor tools for gene expression analysis.

Main Methods:

Detailed walkthrough of microarray data analysis steps.
Includes data import, quality assessment, and preprocessing/normalization.
Covers differential gene expression analysis, gene list comparison, and functional enrichment.

Main Results:

Provides a reproducible workflow for Affymetrix microarray data analysis.
Offers installation instructions for R and Bioconductor packages.
Includes links to sample data and code for practical application.

Conclusions:

Empowers researchers to perform robust gene expression analysis using accessible tools.
Facilitates deeper insights into functional genomics through standardized data analysis.
Aims to make complex microarray data analysis more manageable for scientists.