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.     

Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling

The utility of previously generated microarray data is severely limited owing to small study size, leading to under-powered analysis, and failure of replication. Multiplicity of platforms and various sources of systematic noise limit the ability to compile existing data from similar studies. We present a model for transformation of data across different generations of Affymetrix arrays, developed using previously published datasets describing technical replicates performed with two generations of arrays. The transformation is based upon a probe set-specific regression model, generated from replicate measurements across platforms, performed using correlation coefficients. The model, when applied to the expression intensities of 5069 shared, sequence-matched probe sets in three different generations of Affymetrix Human oligonucleotide arrays, showed significant improvement in inter generation correlations between sample-wide means and individual probe set pairs. The approach was further validated by an observed reduction in Euclidean distance between signal intensities across generations for the predicted values. Finally, application of the model to independent, but related datasets resulted in improved clustering of samples based upon their biological, as opposed to technical, attributes. Our results suggest that this transformation method is a valuable tool for integrating microarray datasets from different generations of arrays.     

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

A new summarization method for Affymetrix probe level data

MOTIVATION: We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. Thereafter, the RNA concentration is estimated from the model. In contrast to previous methods our new method called 'Factor Analysis for Robust Microarray Summarization (FARMS)' supplies both P-values indicating interesting information and signal intensity values. RESULTS: We compare FARMS on Affymetrix's spike-in and Gene Logic's dilution data to established algorithms like Affymetrix Microarray Suite (MAS) 5.0, Model Based Expression Index (MBEI), Robust Multi-array Average (RMA). Further, we compared FARMS with 43 other methods via the 'Affycomp II' competition. The experimental results show that FARMS with default parameters outperforms previous methods if both sensitivity and specificity are simultaneously considered by the area under the receiver operating curve (AUC). We measured two quantities through the AUC: correctly detected expression changes versus wrongly detected (fold change) and correctly detected significantly different expressed genes in two sets of arrays versus wrongly detected (P-value). Furthermore FARMS is computationally less expensive then RMA, MAS and MBEI. AVAILABILITY: The FARMS R package is available from http://www.bioinf.jku.at/software/farms/farms.html. SUPPLEMENTARY INFORMATION: http://www.bioinf.jku.at/publications/papers/farms/supplementary.ps     

TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples

Background

Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.

Results

We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.

Conclusions

TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.     

Exploration,normalization, and summaries of high density oligonucleotide array probe level data

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.     

Limited agreement among three global gene expression methods highlights the requirement for non-global validation

MOTIVATION: DNA microarrays have revolutionized biological research, but their reliability and accuracy have not been extensively evaluated. Thorough testing of microarrays through comparison to dissimilar gene expression methods is necessary in order to determine their accuracy. RESULTS: We have systematically compared three global gene expression methods on all available histologically normal samples from five human organ types. The data included 25 Affymetrix high-density oligonucleotide array experiments, 23 expressed sequence tag based expression (EBE) experiments and 5 SAGE experiments. The reported gene-by-gene expression patterns showed a wide range of correlations between pairs of methods. This level of agreement was sufficient for accurate clustering of datasets from the same tissue and dissimilar methods, but highlights the need for thorough validation of individual gene expression measurements by alternate, non-global methods. Furthermore, analyses of mRNA abundance distributions indicate limitations in the EBE and SAGE methods at both high- and low-expression levels.     

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Summary ChIP‐chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein–DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP‐chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP‐chip data through an Ising model with high‐order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.     

Probe-level measurement error improves accuracy in detecting differential gene expression

MOTIVATION: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes. RESULTS: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression. AVAILABILITY: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalizati...     

Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

Background

Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.

Results

We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.

Conclusion

We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.     

Non-linear analysis of GeneChip arrays

The application of microarray hybridization theory to Affymetrix GeneChip data has been a recent focus for data analysts. It has been shown that the hyperbolic Langmuir isotherm captures the shape of the signal response to concentration of Affymetrix GeneChips. We demonstrate that existing linear fit methods for extracting gene expression measures are not well adapted for the effect of saturation resulting from surface adsorption processes. In contrast to the most popular methods, we fit background and concentration parameters within a single global fitting routine instead of estimating the background before obtaining gene expression measures. We describe a non-linear multi-chip model of the perfect match signal that effectively allows for the separation of specific and non-specific components of the microarray signal and avoids saturation bias in the high-intensity range. Multimodel inference, incorporated within the fitting routine, allows a quantitative selection of the model that best describes the observed data. The performance of this method is evaluated on publicly available datasets, and comparisons to popular algorithms are presented.     

Towards quality control for DNA microarrays.

We present a framework for detecting probes in oligonucleotide microarrays that may add significant error to measurements in hybridization experiments. Four types of so-called degenerate probe behavior are considered: secondary structure formation, self-dimerization, cross-hybridization, and dimerization. The framework uses a well-established model for computing the free energy of nucleic acid sequence hybridization and a novel method for the detection of patterns in hybridization experiment data. Our primary result is the identification of unique patterns in hybridization experiment data that are shown to correlate with each type of degenerate probe behavior. A support function for identifying degenerate probes from a large set of hybridization experiments is given and some preliminary experimental results are given for the Affymetrix HuGeneFL GeneChip. Finally, we show a strong relationship between the Affymetrix discrimination measure for a probe and the free-energy estimate from theoretical models of hybridization. In particular, probes on the HuGeneFL GeneChip with high free-energy estimates (weak hybridization) have almost always approximately zero discrimination. The framework can be applied to any Affymetrix oligonucleotide array, and the software is made freely available to the community.     

Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data

Conventional statistical methods for interpreting microarray data require large numbers of replicates in order to provide sufficient levels of sensitivity. We recently described a method for identifying differentially-expressed genes in one-channel microarray data 1. Based on the idea that the variance structure of microarray data can itself be a reliable measure of noise, this method allows statistically sound interpretation of as few as two replicates per treatment condition. Unlike the one-channel array, the two-channel platform simultaneously compares gene expression in two RNA samples. This leads to covariation of the measured signals. Hence, by accounting for covariation in the variance model, we can significantly increase the power of the statistical test. We believe that this approach has the potential to overcome limitations of existing methods. We present here a novel approach for the analysis of microarray data that involves modeling the variance structure of paired expression data in the context of a Bayesian framework. We also describe a novel statistical test that can be used to identify differentially-expressed genes. This method, bivariate microarray analysis (BMA), demonstrates dramatically improved sensitivity over existing approaches. We show that with only two array replicates, it is possible to detect gene expression changes that are at best detected with six array replicates by other methods. Further, we show that combining results from BMA with Gene Ontology annotation yields biologically significant results in a ligand-treated macrophage cell system.