We collected information from six rice microarray platforms, including the Affymetrix, Agilent 22K and 44K, BGI/Yale, and the NSF 20K and 45K, to construct the ROAD.
We have developed a simple probe-property-independent model, the global average model (GLAM), which we have used to predict absolute target concentration on different microarray platforms, including Affymetrix, Agilent, Illumina and a locally developed custom microarray.
We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray.
Many microarray platforms include multiple probes for a subset of genes.
It also facilitated comparison of datasets across microarray platforms, including for identifying homologous cell types across species as we have pioneered [ 14, 24– 28].
Transcriptome analysis by this method has some significant advantages over microarray platforms, including analysis of polymorphisms in the coding sequence and the ability to detect novel transcripts.
In summary, our results showed that there were moderate, yet significant, correlations between microarray data and MPSS data, while the data from microarray platforms, including the recently included Illumina arrays, generally were well correlated.
In contrast, the NeuroStem Chip includes oligonucleotide probes for 88 genes related to dopaminergic neurons, thus being more comprehensive in this sense, compared to other existing microarray platforms, including focused ones [ 17, 18].
As a result of this study, we recommend using "reference" data sets generated by real-time PCR to evaluate critical aspects of microarray platforms, including signal detection threshold, fold change correlation between pair-wise tissues, profile correlation across multiple tissues, as well as sensitivity and specificity in signal detection and differential expression.
CNV detection in case and control cohorts have been performed using several different microarray platforms, including those utilizing single-nucleotide polymorphisms (SNPs) and others using comparative genomic hybridization (CGH) arrays (Redon et al. 2006; Graubert et al. 2007; Christian et al. 2008; Conrad et al. 2010; Pinto et al. 2011).
