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Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach
Abstract
In this paper, the authors present an assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal universal reference rna (urr) samples to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Toward this target, the authors present an in-silico (binary) optimization process the solutions of which present optimal urr sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.
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