Analysing time course microarray data using
Bioconductor: a case study using yeast2 Aymetrix arrays
Colin S. Gillespie1;2;y, Guiyuan Lei1;2;y, Richard J. Boys1;2,
Amanda Greenall2;3 and Darren J. Wilkinson1;2
October 18, 2010
1School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
2Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University, UK.
3Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle
upon Tyne, NE4 5PL, UK.
yBoth authors contributed equally to the work.
Background: Large scale microarray experiments are becoming increasingly rou-
tine, particularly those which track a number of dierent cell lines through time. This
time-course information provides valuable insight into the dynamic mechanisms un-
derlying 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 dierentially 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.
1 Introduction
As experimental costs decrease, large scale microarray experiments are becoming increasingly rou-
tine, particularly those which track a number of dierent cell lines through time. This is because
time-course information provides valuable insight into the dynamic mechanisms underlying the
biological processes being observed. However, a proper statistical analysis of time-course data re-
quires the use of more sophisticated tools and complex statistical models. For example, problems
due to multiple comparisons are increased by catering for changing eects over time. In this case
study, we demonstrate how to analyse time-course microarray data by investigating a data set
on yeast. We discuss issues related to normalisation, extraction of probesets for specic species,
chip quality,dierential expression and network inference. The freely available software system R
(see [1,2]) has many benets for analysing data of this type and so throughout the analysis we give