a Whole Genome Array Approach - Jacobs University

Background
1.6 Microarray data analysis
Data analysis is an essential process in DNA microarray experiments, since these experiments
normally result in a large amount of information. These data must be adequately processed to
find statistically significant correlations – e.g. co regulation of genes - within and between
different arrays (FIG 4).
First, the hybridisation signal intensities must be filtered and normalised. This transformation
is done to minimise the bias arising from unequal quantities of starting RNA, differences in
labelling or detection efficiencies of the fluorescent dyes applied, and other systematic biases
(Quackenbush 2002). For an overview on different normalisation methods see Foster and
Ghazal 2003(Foster and Ghazal 2003). In the next step, data mining techniques are required to
answer the biological question behind the experiment. Normally, microarray experiments are
conducted to identify genes which are either under- or over-expressed after a shift in the
experimental conditions. For example, we might be interested in genes that have an elevated
expression because of a drug treatment. Such genes are most easily found by simple filtering.
If the log-transformed data (method) is used for filtering, differentially expressed genes are
inferred by a fixed threshold cut off method (i.e. a two-fold increase or decrease). Filtering by
absolute expression change can even be used for experiments, where there are no replicates.
However, there are also ranking-methods available [t-test (Pan 2002), ANOVA (Kerr et al.
2000), Bayesian method or Mann-Whitney test]. All these methods produce errors (falsepositive
and false-negatives), therefore differential gene expression is usually confirmed by
RT-PCR or northern blots (Leung and Cavalieri 2003). In case of interest for co-regulation of
genes (or related arrays), various cluster techniques should be considered. The basic concept
in clustering is to try to identify and group together similarly expressed genes and to correlate
the observations to biology. The idea is that co-regulated and functionally related genes are
grouped into clusters. Some often used grouping techniques are hierarchical clustering (Eisen
et al. 1998), k-means clustering (Soukas et al. 2000), self-organising maps (SOMs) (Kohonen
1992) and principal component analysis (PCA) (Raychaudhuri 2000) (Methods reviews
Quackenbush 2002; Gollub and Sherlock 2006). There is no clustering method that can be
applied for all kinds of experiments. Different cluster methods used on the same data set can
reveal unique aspects of the data (Leung and Cavalieri 2003). It is therefore advisable to
analyse the data using several methods rather than just one (Leung 2002).
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