1. Testing for differential gene expression The following steps of transcription analysis highly depend on the question of the experiment addresses. Nevertheless, it is of interest for most experiments performed which genes are differentially expressed. Although many strategies have been proposed, the most widely used is to set a fixed fold-change cut off. (Cui and Churchill 2003; Yang et al. 2002)

2. Usual values are two- to five-fold because DNA microarrays have been shown to be reproducible at this level, especially when the data are validated by repeated experiments. (DeRisi et al. 1996) To detect more subtle regulations, it is better to calculate the mean and standard deviation for the specific data set. Afterward, every data point can be transformed to its Z score. (Quackenbush 2002)

3. Design of experiments and data verification Replication There are three different kinds of replication that have to be distinguished: 1. Spotting the same probes multiple times on each array. 2. To label and hybridize RNA that has been prepared from one biological experiment several times. 3. Dye switch or dye swap. In a dye switch, the RNA sample that was first Cy-3-labeled is Cy-5-labeled next and vice versa. (Kerr and Churchill 2001; Tseng et al. 2001; Yang and Speed 2002)

4. Good array data depend as much upon good microbial physiology technique as they do on good DNA array technique. (Conway and Schoolnik 2003)

5. Design of experiments • The common case in microarray research is to compare • transcription of two cell populations. Three groups of these • two-condition experiments can be generally distinguished: • Differential response to growth parameters. • 2. Treated v.s. untreated cultures. • 3. Wild-type v.s. mutant strains.

6. If more than two conditions are to be investigated, a single chip experiment is not enough, and several hybridizations have to be combined. (Yang and Speed 2002) It is important to carefully plan this experiment to generate meaningful data, detect possible biases, and avoid that the factors of interest are masked by the adding errors.

7. Basic types of experimental design schemes with multiple samples. Reference design Loop design All-pair design

8. Other experimental approaches with microarrays Beside transcription analysis and many other minor applications, microarrays are routinely used in microbiology to compare genomes and identify microorganisms. These fields of application require the labeling of chromosomal DNA that is hybridized with the array. For the comparison of genomes, also called genomotyping, the chromosomal DNA from the bacterium that has to be typed is labeled.

9. If genes are present in both strains, the corresponding probe will yield a signal in both channels, whereas when the gene is absent in the typed strain, the signal in one channel is missing.

10. Data verification Microarray data can easily contain errors originating from probe interchange, array production, labeling reactions, hybridization, and data acquisition. Therefore, it is crucially advisable to validate data of the most important genes with independent methods to quantify mRNA. Real-time RT-PCR or Northern blotting are the most common options.

11. However, in the majority of studies, microarray data compress the fold changes of expression as compared with real-time RT-PCR by two- to ten-fold. This has been attributed to the smaller dynamic range of microarrays. (Holland 2002;Pappasetal. 2004; Yuenetal. 2002) Northern blotting is another option for data validation, but it is only applicable for much fewer sample numbers and is less quantitative. If a larger number of samples are to be checked, RNA dot blot analysis has been used to verify array data.