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Abstract Background Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most popular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a novel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics and uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are then employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods both in the old and in the newest generation of GeneChips. Results Tissue mixture and Latin Square datasets (provided by Affymetrix) were used to assess the performances of the differential expression analysis depending on the preprocessing strategy. A correlation analysis conducted on the tissue mixture data reveals that the median-polish algorithm allows to best summarize AffyILM concentrations computed at the probe-level. Those correlation results are equivalent to the best correlations observed using popular preprocessing methods relying on intensity values. The performances of each tested preprocessing algorithm were quantified using the Latin Square HG-U133A dataset, thanks to the comparison of differential analysis results with the list of spiked genes. The figures of merit generated illustrates that the performances associated to AffyILM(medianpolish), inferred from the present statistical analysis, are comparable to the best performing strategies previously reported. Conclusions Converting probe intensities to estimates of target concentrations prior to the statistical analysis, AffyILM(medianpolish) is one of the best performing strategy currently available. Using hybridization theory, probe-level estimates of target concentrations should be identically distributed. In the future, a probe-level multivariate analysis of the concentrations should be compared to the univariate analysis of probe-set summarized expression data.

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.

Abstract Background An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations. Results To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log2 fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log2 fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe. Conclusion The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.

Abstract Background Affymetrix Genechips are characterized by probe pairs, a perfect match (PM) and a mismatch (MM) probe differing by a single nucleotide. Most of the data preprocessing algorithms neglect MM signals, as it was shown that MMs cannot be used as estimators of the non-specific hybridization as originally proposed by Affymetrix. The aim of this paper is to study in detail on a large number of experiments the behavior of the average PM/MM ratio. This is taken as an indicator of the quality of the hybridization and, when compared between different chip series, of the quality of the chip design. Results About 250 different GeneChip hybridizations performed at the VIB Microarray Facility for Homo sapiens, Drosophila melanogaster, and Arabidopsis thaliana were analyzed. The investigation of such a large set of data from the same source minimizes systematic experimental variations that may arise from differences in protocols or from different laboratories. The PM/MM ratios are derived theoretically from thermodynamic laws and a link is made with the sequence of PM and MM probe, more specifically with their central nucleotide triplets. Conclusion The PM/MM ratios subdivided according to the different central nucleotides triplets follow qualitatively those deduced from the hybridization free energies in solution. It is shown also that the PM and MM histograms are related by a simple scale transformation, in agreement with what is to be expected from hybridization thermodynamics. Different quantitative behavior is observed on the different chip organisms analyzed, suggesting that some organism chips have superior probe design compared to others.