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One major consideration in designing a microarray experiment involves the RNA source.
Another important consideration in designing a microarray experiment involves the reference (control) group to which gene expression in the population of interest will be compared.
It is often seen that the microarray experiment involves paired samples, for example, a pair of treatment and control samples are from the same individual.
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Using the distributional assumptions presented above, we simulated 1000 realizations of a microarray experiment involving 15 000 genes.
Statistical analysis of the microarray experiment involved two steps: normalization of microarray data and sorting of the genes according to interest.
Each microarray experiment involved dye-reversal replicates, and was performed in duplicate or, in one case, triplicate for the different sets of twins.
TMT contributed to the overall experimental design, conducted the challenge test, the RT-qPCR analysis, performed the microarray experiment, involved in data analysis, drafted and wrote the manuscript.
According to this microarray experiment involving IDC, the lower expression of RAF1 and MYC might correlate with the decreased expression of EGFR.
We have used a very large-scale microarray experiment involving 72 biological replicates to analyze the response of soybean plants to infection by the pathogen Phytophthora sojae and to analyze transcriptional modulation as a result of genotypic variation.
Gene expression changes in the tammar mammary gland during the reproductive cycle were investigated by a large-scale microarray experiment involving 36 comparisons (72 slides including dye swaps, 144 channels in total) [ 69, 102- 104].
Microarray experiments involving heat, oxygen and osmotic downshift were performed in five replicates, while the blood exposure experiment was performed in quadruplicate.
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