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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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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

Microarray data analysis: an overview of design, methodology, and analysis.

Ashani T Weeraratna¹, Dennis D Taub

¹Laboratory of Immunology, National Institutes of Health, National Institute on Aging, Gerontology Research Center, Baltimore, MD, USA.

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

|July 20, 2007

Summary

This chapter outlines basic statistical analysis methods for interpreting complex gene expression data from microarray experiments. It navigates the challenges of analyzing large datasets to understand cellular and experimental variations.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray analysis generates vast gene expression datasets.
Analyzing this high-dimensional data presents significant statistical challenges.
Determining appropriate statistical methods is crucial for accurate interpretation.

Purpose of the Study:

To provide a foundational overview of statistical analysis techniques for microarray data.
To address the complexities and ongoing discussions surrounding data interpretation.

Main Methods:

Review of fundamental statistical approaches for gene expression profiling.
Discussion of various data analysis methodologies applicable to microarray experiments.

Main Results:

Outlines essential statistical concepts for microarray data analysis.
Highlights the evolution of diverse analytical methods to address data complexity.

Conclusions:

Understanding basic statistical analyses is key to effectively interpreting microarray results.
This chapter serves as a guide to navigating the diverse analytical landscape in gene expression studies.