A model of technical variation of microarray signals.

We describe a mathematical model of signal from single-channel direct hybridization microarray platforms. The model establishes a linear relationship between microarray signals and their standard deviations from a minimum set of assumptions. We use the model to precisely define important microarray quality characteristics: resolved fold change and dynamic range. The definitions lead to closed form expressions relating these characteristics to physical parameters of the microarray experiment in the case when both specific and nonspecific binding of target to probe are governed by the Langmuir hybridization isotherm. The predictions of the model are in close agreement to data obtained from spike-in experiments. Given the generality of the model, the introduced definitions of dynamic range and resolved concentration fold-change can be used to conduct cross-platform comparisons and to guide improvement of the microarray platform.     

Evaluation of microarray data normalization procedures using spike-in experiments

Background  

Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods.     

PHOENIX, a web interface for (re)analysis of microarray data

Microarrays are tools to study the expression profile of an entire genome. Technology, statistical tools and biological knowledge in general have evolved over the past ten years and it is now possible to improve analysis of previous datasets. We have developed a web interface called PHOENIX that automates the analysis of microarray data from preprocessing to the evaluation of significance through manual or automated parameterization. At each analytical step, several methods are possible for (re)analysis of data. PHOENIX evaluates a consensus score from several methods and thus determines the performance level of the best methods (even if the best performing method is not known). With an estimate of the true gene list, PHOENIX can evaluate the performance of methods or compare the results with other experiments. Each method used for differential expression analysis and performance evaluation has been implemented in the PEGASE back-end package, along with additional tools to further improve PHOENIX. Future developments will involve the addition of steps (CDF selection, geneset analysis, meta-analysis), methods (PLIER, ANOVA, Limma), benchmarks (spike-in and simulated datasets), and illustration of the results (automatically generated report).     

Microarray data quality - review of current developments

DNA microarray technologies have evolved rapidly to become a key high-throughput technology for the simultaneous measurement of the relative expression levels of thousands of individual genes. However, despite the widespread adoption of DNA microarray technology, there remains considerable uncertainty and scepticism regarding data obtained using these technologies. Comparing results from seemingly identical experiments from different laboratories or even from different days can prove challenging; these challenges increase further when data from different array platforms need to be compared. To comply with emerging regulations, the quality of the data generated from array experiments needs to be clearly demonstrated. This review describes several initiatives that aim to improve confidence in data generated by array experiments, including initiatives to develop standards for data reporting and storage, external spike-in controls, quality control procedures, best practice guidelines, and quality metrics.     

Cross-platform comparison of microarray-based multiple-class prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.     

Ontological analysis of gene expression data: current tools, limitations, and open problems

Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.     

Proteomic analysis of purified Newcastle disease virus particles

Background

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.

Results

The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.

Conclusions

The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.     

Relationship between gene expression and observed intensities in DNA microarrays--a modeling study

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.     

Relationship between gene expression and observed intensities in DNA microarrays—a modeling study

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.     

WebArray: an online platform for microarray data analysis

Background  

Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments.     

Improved statistical analysis of budding yeast TAG microarrays revealed by defined spike-in pools

Saccharomyces cerevisiae knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. TAG arrays report abundance of unique oligonucleotide ‘TAG’ sequences incorporated into each deletion mutation of the yeast knockout collection, allowing measurement of relative strain representation across experimental conditions for all knockout mutants simultaneously. One application of TAG arrays is to perform genome-wide synthetic lethality screens, known as synthetic lethality analyzed by microarray (SLAM). We designed a fully defined spike-in pool to resemble typical SLAM experiments and performed TAG microarray hybridizations. We describe a method for analyzing two-color array data to efficiently measure the differential knockout strain representation across two experimental conditions, and use the spike-in pool to show that the sensitivity and specificity of this method exceed typical current approaches.     

Empirical evaluation of data transformations and ranking statistics for microarray analysis

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.     

Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array

Background

A number of gene-profiling methodologies have been applied to microRNA research. The diversity of the platforms and analytical methods makes the comparison and integration of cross-platform microRNA profiling data challenging. In this study, we systematically analyze three representative microRNA profiling platforms: Locked Nucleic Acid (LNA) microarray, beads array, and TaqMan quantitative real-time PCR Low Density Array (TLDA).

Methodology/Principal Findings

The microRNA profiles of 40 human osteosarcoma xenograft samples were generated by LNA array, beads array, and TLDA. Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms. The endogenous controls/probes contained in each platform have been observed for their stability under different treatments/environments; those included in TLDA have the best performance with minimal coefficients of variation. Importantly, we identify that the proper selection of normalization methods is critical for improving the inter-platform reproducibility, which is evidenced by the application of two non-linear normalization methods (loess and quantile) that substantially elevated the sensitivity and specificity of the statistical data assessment.

Conclusions

Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low. More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.     

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance

Background  

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.     

Diffprot - software for non-parametric statistical analysis of differential proteomics data

Mass spectrometry-based global proteomics experiments generate large sets of data that can be converted into useful information only with an appropriate statistical approach. We present Diffprot - a software tool for statistical analysis of MS-derived quantitative data. With implemented resampling-based statistical test and local variance estimate, Diffprot allows to draw significant results from small scale experiments and effectively eliminates false positive results. To demonstrate the advantages of this software, we performed two spike-in tests with complex biological matrices, one label-free and one based on iTRAQ quantification; in addition, we performed an iTRAQ experiment on bacterial samples. In the spike-in tests, protein ratios were estimated and were in good agreement with theoretical values; statistical significance was assigned to spiked proteins and single or no false positive results were obtained with Diffprot. We compared the performance of Diffprot with other statistical tests - widely used t-test and non-parametric Wilcoxon test. In contrast to Diffprot, both generated many false positive hits in the spike-in experiment. This proved the superiority of the resampling-based method in terms of specificity, making Diffprot a rational choice for small scale high-throughput experiments, when the need to control the false positive rate is particularly pressing.     

Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

Background  

Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations.     

Standardization of protocols in cDNA microarray analysis

Systematic variations can occur at various steps of a cDNA microarray experiment and affect the measurement of gene expression levels. Accepted standards integrated into every cDNA microarray analysis can assess these variabilities and aid the interpretation of cDNA microarray experiments from different sources. A universally applicable approach to evaluate parameters such as input and output ratios, signal linearity, hybridization specificity and consistency across an array, as well as normalization strategies, is the utilization of exogenous control genes as spike-in and negative controls. We suggest that the use of such control sets, together with a sufficient number of experimental repeats, in-depth statistical analysis and thorough data validation should be made mandatory for the publication of cDNA microarray data.