Summaries of Affymetrix GeneChip probe level data

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.     

Quality assessment of the Affymetrix U133A&B probesets by target sequence mapping and expression data analysis

Careful analysis of microarray probe design should be an obligatory component of MicroArray Quality Control (MACQ) project [Patterson et al., 2006; Shi et al., 2006] initiated by the FDA (USA) in order to provide quality control tools to researchers of gene expression profiles and to translate the microarray technology from bench to bedside. The identification and filtering of unreliable probesets are important preprocessing steps before analysis of microarray data. These steps may result in an essential improvement in the selection of differentially expressed genes, gene clustering and construction of co-regulatory expression networks. We revised genome localization of the Affymetrix U133A&B GeneChip initial (target) probe sequences, and evaluated the impact of erroneous and poorly annotated target sequences on the quality of gene expression data. We found about 25% of Affymetrix target sequences overlapping with interspersed repeats that could cause cross-hybridization effects. In total, discrepancies in target sequence annotation account for up to approximately 30% of 44692 Affymetrix probesets. We introduce a novel quality control algorithm based on target sequence mapping onto genome and GeneChip expression data analysis. To validate the quality of probesets we used expression data from large, clinically and genetically distinct groups of breast cancers (249 samples). For the first time, we quantitatively evaluated the effect of repeats and other sources of inadequate probe design on the specificity, reliability and discrimination ability of Affymetrix probesets. We propose that only functionally reliable Affymetrix probesets that passed our quality control algorithm (approximately 86%) for gene expression analysis should be utilized. The target sequence annotation and filtering is available upon request.     

Normalized Affymetrix expression data are biased by G-quadruplex formation

Probes with runs of four or more guanines (G-stacks) in their sequences can exhibit a level of hybridization that is unrelated to the expression levels of the mRNA that they are intended to measure. This is most likely caused by the formation of G-quadruplexes, where inter-probe guanines form Hoogsteen hydrogen bonds, which probes with G-stacks are capable of forming. We demonstrate that for a specific microarray data set using the Human HG_U133A Affymetrix GeneChip and RMA normalization there is significant bias in the expression levels, the fold change and the correlations between expression levels. These effects grow more pronounced as the number of G-stack probes in a probe set increases. Approximately 14% of the probe sets are directly affected. The analysis was repeated for a number of other normalization pipelines and two, FARMS and PLIER, minimized the bias to some extent. We estimate that ～15% of the data sets deposited in the GEO database are susceptible to the effect. The inclusion of G-stack probes in the affected data sets can bias key parameters used in the selection and clustering of genes. The elimination of these probes from any analysis in such affected data sets outweighs the increase of noise in the signal.     

Using high-density exon arrays to profile gene expression in closely related species

Global comparisons of gene expression profiles between species provide significant insight into gene regulation, evolutionary processes and disease mechanisms. In this work, we describe a flexible and intuitive approach for global expression profiling of closely related species, using high-density exon arrays designed for a single reference genome. The high-density probe coverage of exon arrays allows us to select identical sets of perfect-match probes to measure expression levels of orthologous genes. This eliminates a serious confounding factor in probe affinity effects of species-specific microarray probes, and enables direct comparisons of estimated expression indexes across species. Using a newly designed Affymetrix exon array, with eight probes per exon for approximately 315 000 exons in the human genome, we conducted expression profiling in corresponding tissues from humans, chimpanzees and rhesus macaques. Quantitative real-time PCR analysis of differentially expressed candidate genes is highly concordant with microarray data, yielding a validation rate of 21/22 for human versus chimpanzee differences, and 11/11 for human versus rhesus differences. This method has the potential to greatly facilitate biomedical and evolutionary studies of gene expression in nonhuman primates and can be easily extended to expression array design and comparative analysis of other animals and plants.     

Normality of oligonucleotide microarray data and implications for parametric statistical analyses

MOTIVATION: Experimental limitations have resulted in the popularity of parametric statistical tests as a method for identifying differentially regulated genes in microarray data sets. However, these tests assume that the data follow a normal distribution. To date, the assumption that replicate expression values for any gene are normally distributed, has not been critically addressed for Affymetrix GeneChip data. RESULTS: The normality of the expression values calculated using four different commercial and academic software packages was investigated using a data set consisting of the same target RNA applied to 59 human Affymetrix U95A GeneChips using a combination of statistical tests and visualization techniques. For the majority of probe sets obtained from each analysis suite, the expression data showed a good correlation with normality. The exception was a large number of low-expressed genes in the data set produced using Affymetrix Microarray Suite 5.0, which showed a striking non-normal distribution. In summary, our data provide strong support for the application of parametric tests to GeneChip data sets without the need for data transformation.     

Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarrays

Background  

Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls in the original Affymetrix probe annotation have been described; however, few studies have provided rigorous solutions for routine data analysis.