Consequently, data produced so far using different microarray platforms, can only be considered by focusing on the highly differentially expressed genes.
For the many different types of microarray platforms, however, it is difficult to transform raw data so as to accord with any one of the existing frameworks, precluding reliable comparisons among dissimilar data sets.
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.
So far, analyzing gene expression across different microarray platforms remains a challenge.
So we do believe our work, by involving not only 4 different microarray platforms, but more importantly also a deep sequencing technology, does yield new knowledge.
So far, research has mainly focused on gene expression profiling or alterations in oncogenes, even though DNA microarray platforms would allow for high-throughput analyses of copy number alterations (CNAs).
There are two major microarray platforms that have been widely used: cDNA microarrays and oligonucleotide microarrays.
Tan, P. K. et al. Evaluation of gene expression measurements from commercial microarray platforms.
Tan, P.K. et al. Evaluation of gene expression measurements from commercial microarray platforms.
Hester, S.D. et al. Comparison of comparative genomic hybridization technologies across microarray platforms.
The technical reproducibility of microarray platforms is well established.
