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.     

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu     

Libaffy: software for processing Affymetrix GeneChip data

SUMMARY: Affymetrix GeneChip microarrays are increasingly used in gene expression studies and in greater number. A software library was developed that supports Affymetrix file formats and implements two popular summary algorithms (MAS5.0 and RMA). The library is modular in design for integration into larger systems and processing pipelines. Additionally, a graphical interface (GENE) was developed to allow end-user access to the functionality within the library. AVAILABILITY: libaffy is free to use under the GNU GPL license. The source code and Windows binaries can be freely accessed from the website http://src.moffitt.usf.edu/libaffy. Additional API documentation and user manual are available.     

affy--analysis of Affymetrix GeneChip data at the probe level

MOTIVATION: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. RESULTS: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.     

Quality assessment of buccal versus blood genomic DNA using the Affymetrix 500 K GeneChip

Background

Cystic fibrosis (CF) mice, created with a genetically engineered mutation in the Cystic fibrosis transmembrane conductance regulator (Cftr) gene, may develop intestinal plugs which limit their survival past weaning. In a studied population of genetically mixed CF mice differences in allelic ratios at particular loci, between surviving CF mice and mice with the lethal intestinal defect, were used to map cystic fibrosis modifier gene one, Cfm1. Using this approach, we previously identified an X chromosome locus which may influence the survival to weaning of C57BL/6J × BALB/cJ F2 CF mice. We also detected two regions of transmission ratio distortion, independent of Cftr genotype, in a limited dataset. To investigate these findings, in this study we have genotyped 1208 three-week old F2 mice, and 186 day E15.5 embryos, derived from a congenic (C57BL/6J × BALB/cJ) F1 Cftr +/- intercross, for the putative distortion regions.

Results

An excess of homozygous BALB genotypes, compared to Mendelian expectations, was detected on chromosomes 5 (p = 5.7 × 10^-15) and X (p = 3.0 × 10^-35) in three-week old female mice but transmission ratio distortion was not evident in the tested region of chromosome 3 (p = 0.39). Significant pre-weaning lethality of CF mice occurred as 11.3% (137/1208) of the three-week old offspring were identified as CF mice. X chromosome genotypes were not, however, distorted in the female CF mice (p = 0.62), thus the significant non-Mendelian inheritance of this locus was dependent on CF status. The survival of CF embryos to day E15.5 was consistent with Mendelian expectations (42/186 = 23%), demonstrating the loss of CF mice to have occurred between E15.5 and three weeks of age. The excess of X chromosome homozygous BALB genotypes was recorded in female embryos (p = 0.0048), including CF embryos, indicating the distortion to be evident at this age.

Conclusion

Two of three previously suggested loci of transmission ratio distortion were replicated as distorted in this mouse cross. The non-Mendelian inheritance of X chromosome genotypes implicates this region in the survival to weaning of non-CF mice.     

Comparison of Affymetrix GeneChip expression measures

MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Affymetrix GeneChip microarray preprocessing for multivariate analyses

Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.     

BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data

We present Bayesian hierarchical models for the analysis of Affymetrix GeneChip data. The approach we take differs from other available approaches in two fundamental aspects. Firstly, we aim to integrate all processing steps of the raw data in a common statistically coherent framework, allowing all components and thus associated errors to be considered simultaneously. Secondly, inference is based on the full posterior distribution of gene expression indices and derived quantities, such as fold changes or ranks, rather than on single point estimates. Measures of uncertainty on these quantities are thus available. The models presented represent the first building block for integrated Bayesian Analysis of Affymetrix GeneChip data: the models take into account additive as well as multiplicative error, gene expression levels are estimated using perfect match and a fraction of mismatch probes and are modeled on the log scale. Background correction is incorporated by modeling true signal and cross-hybridization explicitly, and a need for further normalization is considerably reduced by allowing for array-specific distributions of nonspecific hybridization. When replicate arrays are available for a condition, posterior distributions of condition-specific gene expression indices are estimated directly, by a simultaneous consideration of replicate probe sets, avoiding averaging over estimates obtained from individual replicate arrays. The performance of the Bayesian model is compared to that of standard available point estimate methods on subsets of the well known GeneLogic and Affymetrix spike-in data. The Bayesian model is found to perform well and the integrated procedure presented appears to hold considerable promise for further development.     

A reanalysis of a published Affymetrix GeneChip control dataset

A response to Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16.     

Feature-level exploration of a published Affymetrix GeneChip control dataset

A comment on Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16.     

SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip

Background  

High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately.     

Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed-effects models

The technology for hybridizing archived tissue specimens and the use of laser-capture microdissection for selecting cell populations for RNA extraction have increased over the past few years. Both these methods contribute to RNA degradation. Therefore, quality assessments of RNA hybridized to microarrays are becoming increasingly more important. Existing methods for estimating the quality of RNA hybridized to a GeneChip, from resulting microarray data, suffer from subjectivity and lack of estimates of variability. In this article, a method for assessing RNA quality for a hybridized array which overcomes these drawbacks is proposed. The effectiveness of the proposed method is demonstrated by the application of the method to two microarray data sets for which external verification of RNA quality is known.     

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.     

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Background  

Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.