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LC contributed to the microarray interpretation and analysis and helped in performing real-time quantitative PCR.
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While we are not the first to ask questions about microarray interpretation of gene expression and log-ratios, our approach is unique in its use of a count-based technology to examine diverse biological samples.
Specific statistical tests used for microarray interpretation are embedded in the corresponding materials and methods section.
It has been demonstrated previously that fold change designations and p-value cutoffs can significantly alter microarray interpretation [ 66].
YFW participated in the Microarray interpretation work, including data organization, cluster analysis, pathway analysis, Q-PCR work, and drafted the manuscript.
The Gene Ontology Annotation project supplies several examples of this, with the improvement of cardiovascular-focused GO annotations having been demonstrated to have led to an evident improvement of microarray interpretation (10).
This paper provides a comprehensive guideline for microarray analysis, interpretation and cross-platform correlation.
Here the question arises as to how to combine these two tests – it was shown recently [ 41] that the cutoffs can significantly alter microarray interpretations.
As shown in Figure 2, up-regulated antioxidant genes in domesticated G. arboreum also show up-regulation at the protein level, validating our microarray interpretations.
JP performed bioinformatics analyses of expression and promoter microarrays, interpretation including annotation, GO analysis.
MB contributed to the microarray analysis and interpretation, and writing of the manuscript.
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