For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task.
Overall, our microarray data gives a comprehensive overview of, and provides new information about, signalling pathways and transcription factors that are regulated by NGF withdrawal.
From pairwise comparisons of microarray and qRT-PCR data, the mean (±SD) Pearson's correlation coefficient for the 12 genes was 0.821 ± 0.191, indicating good overall agreement between the two methods (Additional file 5: Figure S2).> Hierarchical clustering analysis of the normalized microarray data gave three main clusters (Fig. 3).
Partly as a consequence, validation of the microarray data gave variable results, from perfect match, to opposite changes in a few, although effects observed in the microarray, with fold changes as low as 1.2 (e.g., NADH1 and PA2G4) were validated by RT-qPCR.
The microarray data have been deposited in GEO and given the series accession number GSE2167.
Primer sequences are given in Additional file 8, microarray data is given in Additional files 6, 10, 7.
Microarray technologies have been the main platform in recent years [ 10] and microarray expression data gave the opportunity of both assessing thousands of genes at the same time while providing a better understanding of the underlying biological processes of many complex diseases [ 11- 13].
Moreover, microarray expression data gives an overview that the CDCPs might play an important role in stress response/tolerance in Arabidopsis under various stress conditions.
A detailed description of the analysis strategy for the microarray data is given in Supplementary Materials S1.
