When Drew and colleagues [ 80] deprived female zebrafish of food for 21 days and examined gene expression in the livers of the fish in a microarray experiment, they observed that the starved fish showed patterns of gene expression related to ROS responses and energy metabolism similar to those that we observed in DDVP exposed fish.
Moreover, they also ignore the functional association from those functionally related genes in microarray experiments because they assume that biological systems are linear and their approaches follow Normal distribution, respectively.
This workflow has been used by researchers in real microarray experiments and they have reported that the system indeed has been very useful.
The discordance among these results demonstrates that independent microarray experiments, even they use the same platform and study the same biological condition, may not be reproducible [ 18, 19].
Deviations from the behavior, which we describe here, are observed in microarray experiments and they must be due to complexity of DNA target mixtures.
Systematic multiplicative biases are introduced at many stages of microarray experiments, however they can easily be accounted for, which can enable raw data to be directly integrated from different gene expression datasets in order to generate results with improved statistical power and greater biological significance.
They performed a series of microarray experiments in which they took mRNA level measurements for all yeast genes at regular time intervals.
We suspect that these three genes were not identified in the original Qrr4 pulse microarray experiment because they fell below the two-fold cut-off.
To investigate the possibility that pectin catabolism might boost AsA levels, perhaps by increasing flux through the L-galactonic acid pathway, we turned our attention to the transcripts for two putative polygalacturonases (TC175912 and TC184291), which had been overlooked in the original microarray experiment because they were filtered out of the raw data.
There are some disadvantages of microarray experiments in that they do not cover all soybean genes, microarrays cannot identify new genes and there may be errors when expression levels are close to background signals.
