Probe-target hybridization modeling and its application to the analysis of microarrays.

摘要

The application of microarray hybridization theory to Affymetrix GeneChip data has been a recent focus for data analysts. It has been shown that the hyperbolic Langmuir isotherm captures the shape of the signal response to concentration of Affymetrix GeneChips. We suggest a physically motivated hybridization model based on the mechanism of surface adsorption and desorption that accounts for sequence composition of the probes. We have constructed a thermodynamic model for hybridization behavior that we validated in a series of experiments. We demonstrate that existing linear fit methods for extracting gene expression measures are not well adapted for the effect of saturation resulting from surface adsorption processes. In contrast to the most popular methods, we fit background and concentration parameters within a single global fitting routine instead of estimating the background prior to obtaining gene expression measures. We describe a non-linear multi-chip model of the perfect match signal that effectively allows for the separation of specific and non-specific components of the microarray signal and avoids saturation bias in the high intensity range. Multimodel inference, incorporated within the fitting routine, allows a quantitative selection of the model that best describes the observed data. The performance of this method is evaluated on publicly available data sets, and comparisons to popular algorithms are presented. Additionally, these models were applied in a study of proximal causes of aging. Genes that exhibit aging-related changes in Drosophila melanogaster expression were identified in a series of microarray experiments. Transgenic flies were used to analyze the mechanisms of such aging-related gene expression, and to test the effects of specific genes on aging and aging-related deterioration, using Affymetrix microarrays. Advances in understanding the physics of microarray hybridization coupled with novel bioinformatics techniques prompted a new finding in the relationship between aging and stress responses in Drosophila.