Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons

Abstract

Oligonucleotide microarrays are an informative tool to elucidate gene regulatory networks. In order for gene expression levels to be comparable across microarrays, normalization procedures have to be invoked. A large number of methods have been described to correct for systematic biases in microarray experiments. The performance of these methods has been tested only to a limited extend. Here, we evaluate two different types of microarray analyses: (i) the same gene in replicate samples and (ii) different, but co-expressed genes in the same sample. The reliability of the latter analysis needs to be determined for the analysis of regulatory networks and our report is the first attempt to evaluate for the accuracy of different microarray normalization methods in this respect. Consistent with previous results we observed a large effect of the normalization method on the outcome of the expression analyses. Our analyses indicate that different normalization methods should be performed depending on whether a study is aiming to detect differential gene expression between independent samples or whether co-expressed genes should be identified. We make recommendations about the most appropriate method to use.

Figure 1

Graphical representation of the analyses performed in this study. (A) Each sample is analyzed with four different normalization methods and correlation coefficients are calculated for pairwise combinations of normalization methods within one sample. (B) Replicate samples within each strain are analyzed with four different normalization methods and correlation coefficients are calculated between the replicate samples analyzed with a single method. (C) Each of the eight samples (i.e. four E.coli strains × two hybridizations per strain) was analyzed independently under 54 different normalization methods. For each method the correlation in expression level between two operon member genes is calculated.

Figure 2

The influence of different methods is shown for each step in the normalization procedure independently. (A) Mean and 95% confidence intervals of Spearman rank correlation coefficients calculated between the two member genes of an operon. (B) Mean and 95% confidence intervals of Spearman rank correlation coefficients calculated between all genes on replicate arrays.