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Since we and others [22], [40] did not detect IKBKAP among the significantly down-regulated transcripts in FD compared to control samples, we asked whether this discrepancy could be due to a lack of sensitivity of microarray compared to RT-qPCR.

Finally, a higher false positive rate of differential miRNA expression was observed using the microarray compared to the qPCR-array.

A disrupted gene that inhibits growth yields a decreased signal on the microarray compared to the genomic control.

We defined a FER as number of discordant values divided by number of genes analyzed for expression ratio by microarray compared to QPCR using unamplified total RNA.

77 genes with >99% confidence level and >4 fold changes were identified with ADGE microarray, compared to only one such gene with regular microarray (Fig. 3D).

This is possibly due to the long HS exposure time (12 and 24 h) and stage of development (seed imbibition) used in this microarray compared to previous ones [ 43, 63- 65].

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In contrast, the expression pattern for Klf15 exhibits a higher fold-change difference in the microarrays compared to the control.

This is a different batch of microarrays compared to the one we used in the testis experiment.

The high fold change cut-off used for microarray analysis as well as the lower sensitivity of microarrays compared to SSH probably limited the amount of overlapping hits.

The validation procedure did not reveal significant differences between the results obtained with use of microarrays compared to real-time PCR technique [ 10].

Figure 3 shows the corresponding gene expression patterns of four genes: mammaglobin B2 (MGB2), serum amyloid A1 (SAA1), kallikrein-6 (KLK6) and hepsin (HPN) for normal ovary, primary and secondary disease on the microarrays, compared to that on qRT PCR.

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