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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: May 27, 2026

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

Integrative approaches for microarray data analysis.

Levi Waldron¹, Hilary A Coller, Curtis Huttenhower

¹Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.

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

|December 2, 2011

Summary

This summary is machine-generated.

Integrative microarray data analysis combines multiple experiments to overcome the curse of dimensionality. This approach enhances biomarker discovery and gene function prediction by integrating diverse genomic data types.

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Area of Science:

Genomics
Bioinformatics

Background:

Microarrays revolutionized genomics, providing high-throughput gene expression data.
Standardized data repositories and public data sharing became common.
The 'curse of dimensionality' in microarrays poses challenges in distinguishing biological signals from noise.

Purpose of the Study:

To discuss integrative approaches for microarray data analysis.
To highlight applications in biomarker discovery, gene function prediction, and regulatory network inference.
To demonstrate integrating microarray data with other genomic types for advanced biological hypothesis exploration.

Main Methods:

Review of various integrative microarray analysis techniques.
Combining data from multiple microarray experiments to increase sample size and reduce bias.
Integration of large microarray compendia with diverse genomic data.

Main Results:

Integrative analysis enables overcoming the 'curse of dimensionality'.
Facilitates robust biomarker discovery and gene function/interaction prediction.
Enables the exploration of nuanced biological hypotheses through integrated data.

Conclusions:

Integrative microarray analysis is crucial for extracting meaningful biological insights.
Combining diverse genomic data types with microarray compendia unlocks new research avenues.
This approach enhances the power of genomic studies for complex biological questions.