Linear model for fast background subtraction in oligonucleotide microarrays

TL;DR

This paper introduces a fast, linear algebra-based background subtraction algorithm for high-density oligonucleotide microarrays that accounts for neighboring feature correlations and sequence-dependent affinities, improving data preprocessing accuracy.

Contribution

The paper presents a novel linear model for background estimation in microarrays that efficiently incorporates spatial and sequence effects, enhancing preprocessing performance.

Findings

Algorithm is fast and accurate on real data.

Model captures significant physical chemistry aspects.

Strong correlation with experimental and theoretical parameters.

Abstract

One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values. We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model. The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.