Reproducible gene selection algorithm with random effect model in cDNA microarray-based CGH data

cDNA microarray-based CGH with 30 pairs of normal and tumor gastric tissues using cDNA microarrays containing 17,000 human genes was performed to delineate the individual genes that undergo copy-number changes. Frequency analysis is more efficient than mean analysis for detecting subtle differences in copy-number when most of the data are from low spot intensities, such as those seen when performing cDNA microarray-based CGH. This article studies on how to deal with variation of data in replicated measurements for application of frequency analysis. A reproducible gene selection algorithm was developed for minimizing variation across array measurements. This algorithm incorporates a measurement of reproducibility with a random effect model and collects individual genes with reproducible copy-number change as a filtering process. This algorithm controls both reproducibility and number of remaining genes by dropping genes with large variations and results in increased reproducibility. Application of this algorithm allows for obtaining a well-filtered set of genes, thus dealing with variation in frequency analysis of the replicated data.