Unfortunately, the available microarray data from the auxin-induction experiments from Arabidopsis and rice were differently designed so that comparable datasets had to be determined.
These microarray data could be useful for researchers around the world, but unfortunately they are underused.
In theory, biological changes should define the limitations of microarray technology, but unfortunately, technological issues have frequently limited the usefulness of microarray data.
Unfortunately, no resistance pathway was generated from our Arabidopsis microarray data by the Arabidopsis Interactions Viewer program in the bin16 and no significant interlog among genes in bin16 of the data obtained.
Unfortunately, there is no corresponding probe to detect this gene in microarray data.
Unfortunately, this very method is used in an algorithm which estimates missing DNA microarray data by fitting the available data with cumulative-percent-of-variance- selected eigenvectors [Troyanskaya et al., Bioinformatics 17, 520 (2001)].
Unfortunately, none of the five lncRNAs were identified among the differentially expressed lncRNAs based on our microarray data.
TK and RNI analyzed the microarray data.
Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used.
Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis.
