To tackle this challenge, Fujii et al. recently generated a computer model to further study multifocal prostate cancer based on data obtained from 152 human prostatectomy specimens evaluated by DNA microarray analysis, where they demonstrated heterogeneous individual foci with a common clonal precursor [ 76].
This was carried out by a modified two-color microarray analysis where the relative expression of each gene in one niche was calculated in comparison to expression in the previous niche when bacteria moves from the nasopharynx to lungs to blood and then to brain.
This is in sharp contrast to microarray analysis, where expression profile and scope of analysis is predetermined by target design typically employing a limited number of the most commonly characterized genes [17].
Hierarchical clustering is a natural first step in microarray analysis, where it enables a global view of complex microarray data.
Overall, we noted that the magnitude of the difference revealed by QPCR was 20-50% greater than found by microarray analysis (for a subset of genes where both data types were available, e.g., PAL1, C4H1, 4CL1, SuSy2, VIN2, SUT4, GT1-225 and MRP1).
We compared our 484 target genes identified by microarray analysis to the published stem cell database where Ivanova et al. [25] mapped mRNA expression profiles of hematopoietic stem cells of various phenotypically defined hierarchical levels or clusters including LT-HSC, ST-HSC, and early-intermediate-late progenitors.
The expression profiles of paired samples where then compared by microarray analysis using custom spotted oligonucleotide arrays.
A member of the retinol dehydrogenase family, Rdh2, was identified by microarray analysis in polycystic ovary syndrome theca cells [ 29], where it affects steroid synthesis.
As a control of the specificity of STOX1-induced transcriptome alterations, our data were compared with a study where mid-gestation versus normal term placentas were analyzed by microarray analysis [11].
