Software article

Linear model for fast background subtraction in oligonucleotide microarrays

K Myriam Kroll¹ , Gerard T Barkema^2,3 and Enrico Carlon¹

¹  Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200D, Leuven, Belgium

²  Institute for Theoretical Physics, Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, the Netherlands

³  Instituut-Lorentz for Theoretical Physics, University of Leiden, Niels Bohrweg 2, 2333 CA Leiden, the Netherlands

author email corresponding author email

Algorithms for Molecular Biology 2009, 4:15doi:10.1186/1748-7188-4-15

Published:	16 November 2009

Abstract

Background

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.

Results

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.

Conclusion

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.