On probe-level interference and noise modeling in gene expression microarray experiments

This paper describes a signal processing model of gene expression microarray experiments using oligonucleotide technologies. The objective is to estimate the expression transcript concentrations modeled as an analog signal vector. This vector is received via a cascade of two noisy channels that model noise (uncertainty) before, during, and after hybridization. The second channel is also mixing since transcript-probe hybridization is not perfectly specific. The gene expression levels are estimated based on a second-order statistical model that incorporates biological, sample preparation, hybridization, and optical detection noises. A key feature is the explicit modeling of gene-specific and non-specific hybridization in which both have deterministic and random components. The model is applied to the processing of probe pairs as used in Affymetrix arrays, and comparison of currently used methods with the optimum Gauss-Markov estimator. In general, the estimation performance is a function of the hybridization noise characteristics, probe set design and number of experimental replicates, with implications for integrated design of the experimental process.