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/.
Conclusions: CRAN and Bioconductor are stable repositories that provide a wide variety of appropriate statistical tools to analyse time course microarray data.
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| http://dx.doi.org/10.1186/1756-0500-3-81 | DOI Listing |
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