MOTIVATION: Gene expression data exhibit common information over the genome. This article shows how data can be analysed from an efficient whole-genome perspective. Further, the methods have been developed so that users with limited expertise in bioinformatics and statistical computing techniques could use and modify this procedure to their own needs. The method outlined first uses a large-scale linear mixed model for the expression data genome-wide, and then uses finite mixture models to separate differentially expressed (DE) from non-DE transcripts. These methods are illustrated through application to an exceptional UK Brain Expression Consortium involving 12 human frozen post-mortem brain regions. RESULTS: Fitting linear mixed models has allowed variation in gene expression between different biological states (e.g. brain regions, gender, age) to be investigated. The model can be extended to allow for differing levels of variation between different biological states. Predicted values of the random effects show the effects of each transcript in a particular biological state. Using the UK Brain Expression Consortium data, this approach yielded striking patterns of co-regional gene expression. Fitting the finite mixture model to the effects within each state provides a convenient method to filter transcripts that are DE: these DE transcripts can then be extracted for advanced functional analysis. AVAILABILITY: The data for all regions except HYPO and SPCO are available at the Gene Expression Omnibus (GEO) site, accession number GSE46706. R code for the analysis is available in the Supplementary file.

BACKGROUND: In addition to probe sequence characteristics, noise in hybridization array data is thought to be influenced by competitive hybridization between probes tiled at high densities. Empirical evaluation of competitive hybridization and an estimation of what other non-sequence related features might affect noisy data is currently lacking. RESULTS: A high density array was designed to a 1.5 megabase region of the canine genome to explore the potential for probe competition to introduce noise. Multivariate assessment of the influence of probe, segment and design characteristics on hybridization intensity demonstrate that whilst increased density significantly depresses fluorescence intensities, this effect is largely consistent when an ultra high density offset is applied. Signal variation not attributable to sequence composition resulted from the reduction in competition when large inter-probe spacing was introduced due to long repetitive elements and when a lower density offset was applied. Tiling of probes immediately adjacent to various classes of repeat elements did not generate noise. Comparison of identical probe sets hybridized with DNA extracted from blood or saliva establishes salivary DNA as a source of noise. CONCLUSIONS: This analysis demonstrates the occurrence of competitive hybridization between oligonucleotide probes in high density tiling arrays. It supports that probe competition does not generate random noise when it is maintained across a region. To prevent the introduction of noise from this source, the degree of competition should be regulated by minimizing variation in density across the target region. This finding can make an important contribution to optimizing coverage whilst minimizing sources of noise in the design of high density tiling arrays.