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Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples.
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It has been demonstrated that classification gene sets are profoundly influenced by the microarray methodology, such as the microarray technique, microarray platform, and preprocessing methods [ 4- 8].
However, the USA breast cancer dataset [11] and the Netherlands dataset [10] had been obtained from different microarray platforms and also preprocessed using different methods, which makes it practically difficult to evaluate the robustness of the proposed classification method by training the classifier based on one of the datasets and evaluating its performance on the other dataset.
However, it is worthwhile noting that integrating microarray datasets from different studies is not so simple due to the different microarray platforms across different datasets, different experimental protocols, and different preprocessing methods.
As stated above, the use of replicate measurements and/or improvements in data preprocessing can significantly improve the sensitivity of the different genomic microarray platforms.
Despite all preprocessing, it has been shown that data generated in different labs or on different microarray platforms or on different generations of the same platform may not be comparable due to platform or lab specific biases [ 8].
There are two major microarray platforms that have been widely used: cDNA microarrays and oligonucleotide microarrays.
Agilent 8x60K microarray platforms were used for these transcriptome analyses (Xuan et al. 2013).
The combination of two microarray platforms strengthens our database.
To compare the microRNA profile between two different microarray platforms, we used all overlapped microRNAs available in both microarray platforms.
The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms.
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