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Modeling nonlinearity in dilution design microarray data

BIOINFORMATICS 2007年第11期23卷 1339-1347页

作者： Zheng, Xiuwen Huang, Hung-Chung Li, Wenyuan Liu, Peng Li, Quan-Zhen Liu, Ying Univ Texas Dept Math Sci Richardson TX 75083 USA Univ Texas Dept Comp Sci Richardson TX 75083 USA Univ Texas Dept Mol & Cell Biol Richardson TX 75083 USA Iowa State Univ Dept Stat Ames IA 50011 USA Univ Texas Med Ctr Microarray Core Facil Dallas TX 75390 USA

Motivation: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. Results: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.

关键词： .intensities microarray data utilized dilution calibration quantified data analysis microarray expression microarray platforms linearly Modeling nonlinearity sources of nonlinearity RNA mixtures beta ratio adj-FC

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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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A Quasi-linear Approach for microarray Missing Value Imputation

A Quasi-linear Approach for Microarray Missing Value Imputat...

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18th International Conference on Neural Information Processing (ICONIP)

作者： Cheng, Yu Wang, Lan Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka Japan

ISBN: (纸本)9783642249549;9783642249556

Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data. often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.

关键词： microarray data missing value imputation quasi-linear SVR

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Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

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NUCLEIC ACIDS RESEARCH 2007年第16期35卷 e102-e102页

作者： Calza, Stefano Raffelsberger, Wolfgang Ploner, Alexander Sahel, Jose Leveillard, Thierry Pawitan, Yudi Karolinska Inst Dept Med Epidemiol & Biostat Stockholm Sweden Univ Brescia Dept Biomed Sci & Biotechnol Sect Med Stat & Bimetry I-25121 Brescia Italy IGBMC Lab Bioinformat & Genom Integrat Strasbourg France Univ Paris 06 Fac Med Lab Physiopathol Cellulaire & Mol Paris France

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding;also many probe sets are associated with non-differentially- expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.

关键词： .probe sets microarray data discovery rate linear model probe-level un-expressed genes false discoveries FLUSH

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Multi-objective Evolutionary Algorithm for Biclustering in microarrays data

Multi-objective Evolutionary Algorithm for Biclustering in M...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Seridi, Khedidja Jourdan, Laetitia Talbi, El-Ghazali INRIA Lille Nord Europe LIFL CNRS F-59650 Villeneuve Dascq France

ISBN: (纸本)9781424478354

microarrays are a powerful tool in studying genes expressions under several conditions. The obtained data need to be analyzed using data mining methods. Biclustering is a data mining method which consists in simultaneous clustering of rows and columns in a data matrix. Using biclustering, we can extract genes that have similar behavior (co-express) under specific conditions. These genes may share identical biological functions. The aim in analyzing gene expression data is the extraction of maximal number of genes and conditions that present similar behavior. The two objectives to be optimized (size and similarity) are conflicting. Therefore, multi-objective optimization is suitable for biclustering. In our work, we combine a well-known multi-objective genetic algorithm (NSGA-II) with a heuristic to solve the biclutering problem. Due to the huge size of the datasets, we use a string of integers as a solution representation where integers represent the indexes of the rows and the columns. Experimental results on real data set show that our approach can find significant biclusters of high quality.

关键词： Biclustering Multi-objective optimization microarray data

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GO for integration of expression data

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In Silico Biology 2011年第1-2期11卷 11-17页

作者： Dotan-Cohen, Dikla Moonshine, Dana Natan, Moshe Shemer-Avni, Yonat Melkman, Avraham A. Department of Computer Science Ben-Gurion University Be'er-Sheva 84105 Israel Department of Virology and Molecular Development Soroka University Medical Center Ben-Gurion University Be'er-Sheva Israel

The low reproducibility of differential expression of individual genes in microarray experiments has led to the suggestion that experiments be analyzed in terms of gene characteristics, such as GO categories or pathways, in order to enhance the robustness of the results. An implicit assumption of this approach is that the different experiments in effect randomly sample the genes participating in an active process. We argue that by the same rationale it is possible to perform this higher-level analysis on the aggregation of genes that are differentially-expressed in different expression-based studies, even if the experiments used different platforms. The aggregation increases the reliability of the results, it has the potential for uncovering signals that are liable to escape detection in the individual experiments, and it enables a more thorough mining of the ever more plentiful microarray data. We present here a proof-of-concept study of these ideas, using ten studies describing the changes in expression profiles of human host genes in response to infection by Retroviridae or Herpesviridae viral families. We supply a tool (accessible at ***/∼waytogo) which enables the user to learn about genes and processes of interest in this study. © 2011/2012 - IOS Press and the authors. All rights reserved.

关键词： data integration gene expression Gene-Ontology microarray data

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A NEW APPROACH FOR MERGING GENE EXPRESSION dataSETS

A NEW APPROACH FOR MERGING GENE EXPRESSION DATASETS

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Roubaud, Marie-Christine Torresani, Bruno Univ Aix Marseille 1 Lab Anal Topol & Probabilites CMI F-13453 Marseille 13 France

ISBN: (纸本)9781457705700

We propose a new approach for merging gene expression data originating from independent microarray experiments. The proposed approach is based upon a model assuming dataset-independent gene expression distribution, and dataset-dependent observation noise and nonlinear observation functions. The estimation algorithm combines smoothing spline estimation for the observation functions with an iterative method for gene expression estimation. The approach is illustrated by numerical results on simulation studies and real data originating from prostate cancer datasets.

关键词： Gene expression microarray data Smoothing spline regression Empirical Bayes estimation

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Quality assessment of gene selection in microarray data

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OPTIMIZATION METHODS & SOFTWARE 2007年第1期22卷 145-154页

作者： Park, C. H. Jeon, M. Pardalos, P. Park, H. Chungnam Natl Univ Dept Comp Sci & Engn Taejon 305764 South Korea Gwangju Inst Sci & Technol Sch Informat & Mechatron Kwangju 500712 South Korea Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

In microarray data, gene selection can make data analysis efficient and biological interpretations of the selected genes can be very useful. However, microarray data have typically several thousands of genes but only tens of samples, referred to as a small sample-size problem. In this paper, we discuss some problems on gene selection that can occur owing to a small sample size: whether gene selection relying on the extremely small number of samples is reliable and meaningful. Experimental comparisons of well-known three gene selection methods show that classification performances can be very sensitive to training samples and preprocessing steps. We also measure consistency in gene ranking under the changes of training samples or different selection criteria.

关键词： classification feature selection gene selection microarray data

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Quality assessment of gene selection in microarray data

Quality assessment of gene selection in microarray data

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Conference on Systems Analysis, data Mining and Optimization in Biomedicine

关键词： classification feature selection gene selection microarray data

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RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

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BMC GENOMICS 2011年第1期12卷 156-156页

作者： Hruz, Tomas Wyss, Markus Docquier, Mylene Pfaffl, Michael W. Masanetz, Sabine Borghi, Lorenzo Verbrugghe, Phebe Kalaydjieva, Luba Bleuler, Stefan Laule, Oliver Descombes, Patrick Gruissem, Wilhelm Zimmermann, Philip ETH Dept Biol CH-8092 Zurich Switzerland ETH Inst Theoret Comp Sci CH-8092 Zurich Switzerland Univ Geneva NCCR Frontiers Genet CH-1211 Geneva 4 Switzerland TUM D-85354 Freising Weihenstephan Germany NEBION AG CH-8048 Zurich Switzerland UWA Ctr Med Res Western Australian Inst Med Res Perth WA Australia

Background: RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts. Results: Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions. Conclusion: We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://***.

关键词： Reference Gene microarray data Tissue Type Lymphoblastoid Cell Line Candidate Reference Gene

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