Investigating the role of the JAK/STAT and MAPK ... - UCL Discovery

4.7 Validation of Microarray Data by qPCR
Before making any general inferences on regulation of specific genes, qPCR analysis was
carried out in order to confirm some of the gene changes seen on the microarray. Fig 4.11a
shows a list of 23 genes which were tested for differential expression between sham and I/R.
With this data at hand, the effect of three common algorithms were examined to ensure that
GCRMA was indeed the best method of normalization to choose for this dataset. Both
GCRMA and RMA normalizations were carried out in Bioconductor and MAS5.0
normalization was carried out using Genespring 7.3. The log of the microarray fold change
between sham and I/R was plotted on the x-axis versus the log qPCR fold change on the y-
axis. Nonlinear regression analysis was used to test the correlation between the microarray
data and qPCR levels (Fig 4.11b,c,d). Taking all 23 genes into account, the value was
highest for GCRMA at 0.947, followed by RMA at 0.943 and MAS 5.0 at 0.857. With a few
exceptions (Bcl-2, XIAP), the qPCR data verifies the microarray results and thus some
general conclusions can be drawn from the dataset as a whole. The following sections
therefore will deal with regulation of specific genes and what effects they may have on I/R
injury. It must be noted here that due to sample problems, it was not possible to confirm the
gene expression changes seen with Ucn2 by qPCR.
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