Robust background normalization method for one-channel microarrays

Abstract

AbstractBackground:Microarray technology, aims to measure the amount of changes in transcripted messages for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different experimental conditions, these measurements can include both systematic and random erroneous signals. For this reason, we present a novel gene expression index, called multi-RGX (Multiple-probe Robust Gene Expression Index) for one-channel microarrays.Methods:Multi-RGX, different from other gene expression indices, considers the long-tailed symmetric (LTS) density, covering a wider range of distributions for modelling gene expressions on the log-scale, resulting in robust inference and it takes into account both probe and gene specific intensities. Furthermore, we derive the covariance-variance matrix of model parameters from the observed Fisher information matrix and test the performance of the multi-RGX method in three different datasets.Results:Our method is found to be a promising method regarding its alternatives in terms of accuracy and computational time.Conclusion:Multi-RGX gives accurate results with respect to its alternatives, with a reduction in computational cost.