illuminaio: An open source IDAT parsing tool for Illumina microarrays

The IDAT file format is used to store BeadArray data from the myriad of genomewide profiling platforms on offer from Illumina Inc. This proprietary format is output directly from the scanner and stores summary intensities for each probe-type on an array in a compact manner. A lack of open source tools to process IDAT files has hampered their uptake by the research community beyond the standard step of using the vendor’s software to extract the data they contain in a human readable text format. To fill this void, we have developed the illuminaio package that parses IDAT files from any BeadArray platform, including the decryption of files from Illumina’s gene expression arrays. illuminaio provides the first open-source package for this task, and will promote wider uptake of the IDAT format as a standard for sharing Illumina BeadArray data in public databases, in the same way that the CEL file serves as the standard for the Affymetrix platform.

Meta-Analysis Combines Affymetrix Microarray Results Across Laboratories

With microarray technology becoming more prevalent in recent years, it is now common for several laboratories to employ the same microarray technology to identify differentially expressed genes that are related to the same phenomenon in the same species. Although experimental specifics may be similar, each laboratory will typically produce a slightly different list of statistically significant genes, which calls into question the validity of each gene list (i.e. which list is best). A statistically-based meta-analytic approach to microarray analysis systematically combines results from the different laboratories to provide a single estimate of the degree of differential expression for each gene. This approach provides a more precise view of genes that are of significant interest, while simultaneously allowing for differences between laboratories. The widely-used Affymetrix oligonucleotide array and its software are of particular interest because the results are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the utility of such an approach in combining microarray results across laboratories.