This observation allows the conclusion that already the technical noise component in microarray data, which is the only remaining source of variation in the golden-spike data, requires considering robust models.
We tested this hypothesis by calculating principal components using microarray expression profiles of RNA isolated from mechanically ventilated patients at risk for pneumonia.
Figure 2A shows the first three principal components of microarray analysis data (PC1, PC2 and PC3) in X, Y and Z respectively, and demonstrate correlation between the expression profile of the three single neurons per group (Mature Red # 1 3, Old blue# 1 3).
The three main components, ESTs, microarrays and the bioinformatics tools, are listed here.
Here, we used the SVM to improve this microarray component.
Genes with little or no annotation represent a gene discovery component to future microarray experiments.
Applications of gene component methodology for microarray studies had been reported in literature.
Improving this microarray component could thus directly add to the performance of the integrative system.
Principal component analysis of microarray results revealed that the first two components accounted for the 89% of total variation.
Novel analytical tools have been constructed to address every component of the microarray experiment and optimize performance [ 6].
