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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 14, 2026

Microarray Analysis for Saccharomyces cerevisiae

Microarray Analysis for Saccharomyces cerevisiae

Published on: April 7, 2011

Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays.

Colin S Gillespie¹, Guiyuan Lei, Richard J Boys

¹School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. c.gillespie@ncl.ac.uk.

BMC Research Notes

|March 23, 2010

Summary

This summary is machine-generated.

This study demonstrates how to analyze time-course microarray data using R. Open-source repositories like CRAN and Bioconductor offer tools for detecting gene expression changes and generating biological pathways.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Time-course microarray experiments are common for studying dynamic biological processes.
Analyzing this complex data requires advanced statistical methods and tools.

Purpose of the Study:

To provide a protocol and example analysis for time-course microarray data.
To illustrate methods for detecting differentially expressed genes and generating pathways.

Main Methods:

Utilizing the R programming language with open-source repositories CRAN and Bioconductor.
Constructing statistical contrasts to identify significant gene expression changes over time.
Applying methods for pathway inference from time-series gene expression data.

Main Results:

Demonstrated practical application of statistical tools for time-course microarray analysis.
Provided R commands for reproducible research and further analysis.
Highlighted the utility of CRAN and Bioconductor for this type of data.

Conclusions:

CRAN and Bioconductor are robust repositories for statistical tools.
These resources facilitate the analysis of time-course microarray data, enabling deeper biological insights.