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Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data

BMC BIOINFORMATICS 2007年第1期8卷 1-14页

作者： Chen, James J. Hsueh, Huey-Miin Delongchamp, Robert R. Lin, Chien-Ju Tsai, Chen-An US FDA Natl Ctr Toxicol Res Div Personalized Nutr & Med Jefferson AR 72079 USA Natl Chengchi Univ Dept Stat Taipei 11623 Taiwan Univ Arkansas Med Sci Dept Epidemiol Little Rock AR 72205 USA China Med Univ Dept Publ Hlth Taichung Taiwan China Med Univ Ctr Biostat Taichung Taiwan

Background: Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the microarray quality control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC. Results: A total of 293 arrays were used in the intra- and inter-platform analysis. A hierarchical cluster analysis shows distinct differences in the measured intensities among the five platforms. A number of genes show a small fold-change in one platform and a large fold-change in another platform, even though the correlations between platforms are high. An analysis of variance shows thirty percent of gene expressions of the samples show inconsistent patterns across the five platforms. We illustrated that POG does not reflect the accuracy of a selected gene list. A non-overlapping gene can be truly differentially expressed with a stringent cut, and an overlapping gene can be non-differentially expressed with non-stringent cutoff. In addition, POG is an unusable selection criterion. POG can increase or decrease irregularly as cutoff changes;there is no criterion to determine a cutoff so that POG is optimized. Conclusion: Using various statistical methods we demonstrate that there are differences in the intensities measured by different platforms and diffe

关键词： microarray Platform Gold Standard Data microarray quality control MAQC Project Individual Platform

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Evaluating methods for ranking differentially expressed genes applied to microarray quality control data

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BMC BIOINFORMATICS 2011年第1期12卷 1-13页

作者： Kadota, Koji Shimizu, Kentaro Univ Tokyo Grad Sch Agr & Life Sci Agr Bioinformat Res Unit Bunkyo Ku Tokyo 1138657 Japan

Background: Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility. Results: We compared the eight gene ranking methods using the microarray quality control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated t statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra-and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra-and inter-platform reproducibility of WAD was also observed at a higher biological function level. Conclusion: These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.

关键词： microarray Platform Enrichment Score Rank Product microarray quality control Rank Gene List

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Normalization and technical variation in gene expression measurements

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JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 2006年第5期111卷 361-372页

作者： Liggett, Walter NIST Gaithersburg MD 20899 USA

Using data from the microarray quality control (MAQC) project, we demonstrate two data-analysis methods that shed light on the normalization of gene expression measurements and thereby on their technical variation. One is an improved method for normalization of multiple assays with mRNA concentrations related by a parametric model. The other is a method for characterizing limitations on the effectiveness of normalization in reducing technical variation. We apply our improved normalization to the four project materials as part of testing the linearity of the probe responses. We find that the lack of linearity is statistically significant but small enough that its sources cannot be easily identified. Applying our characterization method to assays of the same material, we show that there is a source of variation that cannot be eliminated by normalization and therefore must be dealt with by other means. Four high-density, single probe, one-color microarray platforms underlie our demonstration.

关键词： calibration linearity classifier performance high-density microarrays inter-laboratory study latent variables MAQC microarray quality control simultaneous F tests sources of variation statistical dependence

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Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships

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GENOME BIOLOGY 2007年第3期8卷 R44-R44页

作者： Dabney, Alan R. Storey, John D. Univ Washington Dept Biostat Seattle WA 98195 USA Univ Washington Dept Genome Sci Seattle WA 98195 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

In normalizing two-channel expression arrays, the ANOVA approach explicity incorporates the experimental design in its model, and the MA-plot based approach accounts for intensivity-dependent biases. However, both approaches can lead to inacurate normalization in fairly common scenarios. We propose a method called eCADS for normalizing two-channel microarrays that accounts for both experimental design and intensity-dependent biases. Under reasonable experimental designs, eCADS preserves differential expression relationships and requires only a single array per sample pair.

关键词： Additional Data File Array Function microarray quality control Array Effect Direct Comparison Experiment

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How high is the level of technical noise in microarray data?

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BIOLOGY DIRECT 2007年第1期2卷 1-9页

作者： Klebanov, Lev Yakovlev, Andrei Charles Univ Prague Dept Probabil & Stat CZ-18675 Prague 8 Czech Republic Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA

Background: microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the microarray quality control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data. Results: In the present report, the MAQC study is reanalyzed in order to quantitatively assess measurement errors inherent in high-density oligonucleotide array technology (Affymetrix platform). The level of noise is directly estimated from technical replicates of gene expression measurements in the absence of biological variability. For each probe set, the magnitude of random fluctuations across technical replicates is characterized by the standard deviation of the corresponding log-expression signal. The resultant standard deviations appear to be uniformly small and symmetrically distributed across probe sets. The observed noise level does not cause any tangible bias in estimated pair-wise correlation coefficients, the latter being particularly prone to its presence in microarray data. Conclusion: The reported analysis strongly suggests that, contrary to popular belief, the random fluctuations of gene expression signals caused by technical noise are quite low and the effect of such fluctuations on the results of statistical inference from Affymetrix GeneChip microarray data is negligibly small.

关键词： microarray Data Technical Noise Affymetrix Platform microarray quality control Affymetrix microarray Data

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Boundary distinction interpretation of microarray data via discrete correlate summation

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GENOME BIOLOGY 2011年第SUPPL 1期12卷 16-17页

作者： Westwood, Brian M. Olex, Amy L. Norris, James L. Poole, Leslie B. Fetrow, Jacquelyn S. Wake Forest Univ Dept Mol Genet & Genom Sch Med Winston Salem NC 27157 USA Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA Wake Forest Univ Dept Biochem Sch Med Winston Salem NC 27157 USA Wake Forest Univ Dept Phys Winston Salem NC 27109 USA

P27

关键词： Ergosterol Butanoate Hydrogen Peroxide Stressor microarray quality control Major Biological Process

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Evaluation of inter-laboratory and cross-platform concordance of DNA microarrays throuht discrimanating genes classifier transferability

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Journal of Bioinformatics and Computational Biology 2009年第1期7卷 157-173页

作者： Mao, Shihong Wang, Charles Dong, Guozhu Department of Computer Science and Engineering Wright State University Dayton OH 45435 United States Department of Medicine and Burns Allen Research Institute Cedars-Sinai Medical Center Los Angeles CA 90048 United States David Geffen School of Medicine UCLA Los Angeles CA 90048 United States

microarray technology has great potential for improving our understanding of biological processes, medical conditions, and diseases. Often, microarray datasets are collected using different microarray platforms (provided by different companies) under different conditions in different laboratories. The cross-platform and cross-laboratory concordance of the microarray technology needs to be evaluated before it can be successfully and reliably applied in biological/clinical practice. New measures and techniques are proposed for comparing and evaluating the quality of microarray datasets generated from different platforms/laboratories. These measures and techniques are based on the following philosophy: the practical usefulness of the microarray technology may be confirmed if discriminating genes and classifiers, which are the focus of most, if not all, comparative investigations, discovered/trained from data collected in one lab/platform combination can be transferred to another lab/platform combination. The rationale is that the nondiscriminating genes might not be as strongly regulated as the discriminating genes, by the biological process of the tissue cells under study, and hence they may behave more randomly than the discriminating genes. Our experiment results, on microarray datasets generated from different platforms/laboratories using the reference mRNA samples in the microarray quality control (MAQC) project, showed that DNA microarrays can produce highly repeatable data in a cross-platform cross-lab manner, when one focuses on the discriminating genes and classifiers. In our comparative study, we compare samples of one type against samples of another type;the methodology can be applied to situations where one compares one arbitrary class of data against another. Other findings include: (1) using three discriminating-gene/ classifier-based methods to test the concordance between microarray datasets gave consistent results;(2) when noisy (nondiscriminating) gen

关键词： Classifier transferability Concordance Discriminating gene transferability microarray quality control microarray technology

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Extended analysis of benchmark datasets for Agilent two-color microarrays

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BMC BIOINFORMATICS 2007年第1期8卷 371-371页

作者： Kerr, Kathleen F. Univ Washington Dept Biostat Seattle WA 98195 USA

Background: As part of its broad and ambitious mission, the microarray quality control (MAQC) project reported the results of experiments using External RNA controls (ERCs) on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray. Results: A comparison of different methods of processing Agilent two-color data shows substantial differences among methods for low-intensity genes. The sensitivity and specificity for detecting differentially expressed genes varies substantially for different methods. Analysis also reveals that the ERCs in the MAQC data only span the upper half of the intensity range, and therefore cannot be representative of all genes on the microarray. Conclusion: Although ERCs demonstrate good agreement between observed and expected logratios on the Agilent two-color platform, such an analysis is incomplete. Simple loess normalization outperformed data processing with Agilent's Feature Extraction software for accurate identification of differentially expressed genes. Results from studies using ERCs should not be overgeneralized when ERCs are not representative of all probes on a microarray.

关键词： Data Processing Method Feature Extraction Software Loess Normalization Background Adjustment microarray quality control

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Utilization of AFFX spike-in control probes to monitor sample identity throughout Affymetrix GeneChip Array processing

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BIOTECHNIQUES 2010年第5期48卷 371-378页

作者： Walter, Michael Honegger, Anja Schweizer, Rahel Poths, Sven Bonin, Michael Univ Tubingen Inst Human Genet Microarray Facil Tubingen D-72076 Tubingen Germany

microarrays evolved from a highly specialized technique into a standard molecular biology method that is widely used for whole-genome gene expression profiling. One of the most important aspects of this method is sample identity, that is, whether the expression profile recorded from an array actually derives from the indicated sample. Several potential steps in the protocol exist where a mix-up of samples may occur. With the increasing size of microarray studies, it is important to ensure that each expression profile is assigned to the correct sample. Errors at this level almost certainly lead to erroneous results and can even cause a complete failure of the microarray study. We developed a system that utilizes probes already present on commercially available Affymetrix arrays to unambiguously correlate the recorded expression profile with the input sample RNA. A set of eight spike-in controls were generated, which can be added to sample RNA in different combinations to generate an "on-chip identifier" that passes through the entire array processing protocol and results in a sample-specific hybridization pattern. This pattern can then be used to monitor whether each array was hybridized with the correct sample. The spike-in controls did not have any negative effect on RNA integrity or any detectable influence on the expression values of the remaining probes on the array;therefore, these controls represent an inexpensive and easily adaptable system to guarantee high-quality results from microarray experiments.

关键词： Affymetrix GeneChip microarray hybridization gene expression profiling sample tracking microarray quality control

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Investigation of reproducibility of differentially expressed genes in DNA microarrays through statistical simulation

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BMC proceedings 2009年第2-Sup期3 Suppl 2卷 S4页

作者： Xiaohui Fan Leming Shi Hong Fang Stephen Harris Roger Perkins Weida Tong National Center for Toxicological Research (NCTR) US Food and Drug Administration 3900 NCTR Rd Jefferson AR 72079 USA. fanxh@***

Recent publications have raised concerns about the reliability of microarray technology because of the lack of reproducibility of differentially expressed genes (DEGs) from highly similar studies across laboratories and platforms. The rat toxicogenomics study of the microarray quality control (MAQC) project empirically revealed that the DEGs selected using a fold change (FC)-based criterion were more reproducible than those derived solely by statistical significance such as P-value from a simple t-tests. In this study, we generate a set of simulated microarray datasets to compare gene selection/ranking rules, including P-value, FC and their combinations, using the percentage of overlapping genes between DEGs from two similar simulated datasets as the measure of reproducibility. The results are supportive of the MAQC's conclusion on that DEG lists are more reproducible across laboratories and platforms when FC-based ranking coupled with a nonstringent P-value cutoff is used for gene selection compared with selection based on P-value based ranking method. We conclude that the MAQC recommendation should be considered when reproducibility is an important study objective.

关键词： Fold Change Gene Selection Method Fold Change Cutoff microarray quality control Base Ranking Method

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