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

Colin S Gillespie12*, Guiyuan Lei12, Richard J Boys12, Amanda Greenall23 and Darren J Wilkinson12

Author Affiliations

1 School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

2 Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University, UK

3 Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK

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BMC Research Notes 2010, 3:81  doi:10.1186/1756-0500-3-81

Published: 19 March 2010

Abstract

Background

Large scale microarray experiments are becoming increasingly routine, particularly those which track a number of different cell lines through time. This time-course information provides valuable insight into the dynamic mechanisms underlying the biological processes being observed. However, proper statistical analysis of time-course data requires the use of more sophisticated tools and complex statistical models.

Findings

Using the open source CRAN and Bioconductor repositories for R, we provide example analysis and protocol which illustrate a variety of methods that can be used to analyse time-course microarray data. In particular, we highlight how to construct appropriate contrasts to detect differentially expressed genes and how to generate plausible pathways from the data. A maintained version of the R commands can be found at http://www.mas.ncl.ac.uk/~ncsg3/microarray/ webcite.

Conclusions

CRAN and Bioconductor are stable repositories that provide a wide variety of appropriate statistical tools to analyse time course microarray data.