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Two-stage normalization using background intensities in cdna microarray data

BMC BIOINFORMATICS 2004年第1期5卷 97-97页

作者： Yoon, D Yi, SG Kim, JH Park, T Seoul Natl Univ Coll Nat Sci Dept Stat Seoul 151747 South Korea Seoul Natl Univ Program Bioinformat Seoul 151747 South Korea Seoul Natl Univ Sch Med SNUBI Seoul 110799 South Korea

Background: In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process. Results: In this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods. Conclusions: For the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image an

关键词： Normalization Method Background Intensity Background Measure LOWESS Normalization cdna microarray data

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Jointly analyzing gene expression and copy number data in breast cancer using data reduction models

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2006年第1期3卷 2-16页

作者： Berger, JA Hautaniemi, S Mitra, SK Astola, J Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA MIT Dept Biol Engn Cambridge MA 02139 USA Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle theta in the GSVD. With the proper choice of theta, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide variety of joint copy number and expression-based studies. Supplementary information is available online, including software implementations and experimental data.

关键词： generalized singular value decomposition cdna microarray data CGH microarray data gene expression DNA copy numbers breast cancer

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Light-dependent regulation of cell division in Ostreococcus:: Evidence for a major transcriptional input

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PLANT PHYSIOLOGY 2007年第3期144卷 1360-1369页

作者： Moulager, Mickael Monnier, Annabelle Jesson, Beline Bouvet, Regis Mosser, Jean Schwartz, Christian Garnier, Lionel Corellou, Florence Bouget, Francois-Yves Univ Paris 06 CNRS UMR 7628 Lab Arago F-66650 Banyuls sur Mer France GFAS IFR 140 OUEST Genopole Transcriptome Platform Fac Med F-35043 Rennes France Ctr Hosp Mol Genet & Biochem Dept Med Genom Unit F-35043 Rennes France Univ Rennes 1 Fac Med CNRS UMR Genet & Dev 6061IFR GFAS 140CS 34317 F-35043 Rennes France

Cell division often occurs at specific times of the day in animal and photosynthetic organisms. Studies in unicellular photosynthetic algae, such as Chlamydomonas or Euglena, have shown that the photoperiodic control of cell division is mediated through the circadian clock. However, the underlying mechanisms remain unknown. We have studied the molecular basis of light-dependent control of cell division in the unicellular green alga Ostreococcus. We found that cell division obeys a circadian oscillator in Ostreococcus. We provide evidence suggesting that the clock may, at least in part, regulate directly cell division independently of the metabolism. Combined microarray and quantitative real-time reverse transcription-polymerase chain reaction analysis of the main core cell cycle gene expression revealed an extensive transcriptional regulation of cell division by the photoperiod in Ostreococcus. Finally, transcription of the main core cell cycle genes, including cyclins and cyclin-dependent kinases, was shown to be under circadian control in Ostreococcus, suggesting that these genes are potential targets of the circadian clock in the control of cell division.

关键词： cdna microarray data CHLAMYDOMONAS-REINHARDTII CIRCADIAN CLOCK CYCLE EXPRESSION PLANTS GENES TAURI

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Reduced expression of succinyl-coenzyme A ligase can be compensated for by up-regulation of the γ-aminobutyrate shunt in illuminated tomato leaves

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PLANT PHYSIOLOGY 2007年第3期145卷 626-639页

作者： Studart-Guimaraes, Claudia Fait, Aaron Nunes-Nesi, Adriano Carrari, Fernando Usadel, Bjoern Fernie, Alisdair R. Max Planck Inst Mol Pflanzenphysiol Dept Willmitzer & Stitt D-14476 Golm Germany

Increasing experimental evidence suggests that the tricarboxylic acid cycle in plants is of greater importance in illuminated photosynthetic tissues than previously thought. In this study, transgenic tomato (Solanum lycopersicum) plants expressing a fragment of the beta-subunit of succinyl-coenzyme A ligase in either the antisense orientation or using the RNA interference approach, however, revealed little alteration in either photosynthesis or plant growth despite exhibiting dramatic reductions in activity. Moreover, the rate of respiration was only moderately affected in the transformants, suggesting that this enzyme does not catalyze a crucial step in mitochondrial respiration. However, metabolite and transcript profiling of these lines alongside enzyme and label redistribution experiments revealed that, whereas considerable activity of this enzyme appears to be dispensable, the reason for such a mild phenotype in extremely inhibited lines was an up-regulation of an alternative pathway for succinate production -that offered by the gamma-aminobutyric acid shunt. When taken together, these data highlight the importance both of succinate production for mitochondrial metabolism and the interplay between various routes of its production. The results are discussed in the context of current models of plant respiration in mitochondrial and cellular metabolism of the illuminated leaf.

关键词： PYRUVATE-DEHYDROGENASE COMPLEX cdna microarray data NITRATE ASSIMILATION PHOTOSYNTHETIC PERFORMANCE SEMIALDEHYDE DEHYDROGENASE MITOCHONDRIAL METABOLISM CARBOHYDRATE-METABOLISM RESPIRATORY METABOLISM CARBON ASSIMILATION ELECTRON-TRANSPORT

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Functional genomics of the β-cell:: Short-chain 3-hydroxyacyl-coenzyme A dehydrogenase regulates insulin secretion independent of K⁺ currents

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MOLECULAR ENDOCRINOLOGY 2007年第3期21卷 765-773页

作者： Hardy, Olga T. Hohmeier, Hans E. Becker, Thomas C. Manduchi, Elisabetta Doliba, Nicolai M. Gupta, Rana K. White, Peter Stoeckert, Christian J., Jr. Matschinsky, Franz M. Newgard, Christopher B. Kaestner, Klaus H. Univ Penn Dept Genet Sch Med Philadelphia PA 19104 USA Univ Penn Dept Biochem & Biophys Sch Med Philadelphia PA 19104 USA Duke Univ Med Ctr Sarah W Stedman Nutr & Metab Ctr Durham NC 27704 USA Duke Univ Med Ctr Dept Pharmacol Durham NC 27704 USA Duke Univ Med Ctr Dept Canc Biol Durham NC 27704 USA Duke Univ Med Ctr Dept Med Durham NC 27704 USA Univ Penn Computat Biol & Informat Lab Philadelphia PA 19104 USA

Recent advances in functional genomics afford the opportunity to interrogate the expression profiles of thousands of genes simultaneously and examine the function of these genes in a high-throughput manner. In this study, we describe a rational and efficient approach to identifying novel regulators of insulin secretion by the pancreatic beta-cell. Computational analysis of expression profiles of several mouse and cellular models of impaired insulin secretion identified 373 candidate genes involved in regulation of insulin secretion. Using RNA interference, we assessed the requirements of 10 of these candidates and identified four genes (40%) as being essential for normal insulin secretion. Among the genes identified was Hadhsc, which encodes short-chain 3-hydroxyacyl-coenzyme A dehydrogenase (SCHAD), an enzyme of mitochondrial beta-oxidation of fatty acids whose mutation results in congenital hyperinsulinism. RNA interference-mediated gene suppression of Hadhsc in insulinoma cells and primary rodent islets revealed enhanced basal but normal glucose-stimulated insulin secretion. This increase in basal insulin secretion was not attenuated by the opening of the K-ATP channel with diazoxide, suggesting that SCHAD regulates insulin secretion through a K-ATP channel-independent mechanism. Our results suggest a molecular explanation for the hyperinsulinemia hypoglycemic seen in patients with SCHAD deficiency.

关键词： cdna microarray data WIDE RNAI ANALYSIS HYPERINSULINEMIC HYPOGLYCEMIA GLUCOSE-HOMEOSTASIS GENE-EXPRESSION FATTY-ACIDS DROSOPHILA INTERFERENCE ISLETS MICE

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Considerations for the processing and analysis of GoldenGate-based two-colour Illumina platforms

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STATISTICAL METHODS IN MEDICAL RESEARCH 2009年第5期18卷 437-452页

作者： Lynch, A. G. Dunning, M. J. Iddawela, M. Barbosa-Morais, N. L. Ritchie, M. E. Univ Cambridge Cambridge Res Inst Canc Res UK Li Ka Shing Ctr Cambridge CB2 0RE England

Illumina's GoldenGate technology is a two-channel microarray platform that allows for the simultaneous interrogation of about 1 500 locations in the genome. GoldenGate has proved a flexible platform not only in the choice of those 1 500 locations, but also in the choice of the property being measured at them. It retains the desirable properties of Illumina's BeadArrays in that the probes (in this case 'beads') are randomly arranged across the microarray, there are multiple instances of each probe and many samples can be processed simultaneously. As for other Illumina technologies, however, these properties are not exploited as they might be. Here we review the various common adaptations of the GoldenGate platform, review the analysis methods that are associated with each adaptation and then, with the aid of a number of example data sets we illustrate some of the improvements that can be made over the default analysis.

关键词： UNIVERSAL BEAD ARRAYS cdna microarray data GENE-EXPRESSION HUMAN GENOME NORMALIZATION VARIANCE TISSUES SINGLE COMMON CELLS Genome Human Cells Platform normalization microarray Variance

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Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments

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GENOME BIOLOGY 2002年第12期3卷 research0071.1-research0071.16页

作者： Townsend, Jeffrey P. Hartl, Daniel L. Harvard Univ Dept Organism & Evolutionary Biol Cambridge MA 02138 USA

Background: Methods of microarray analysis that suit experimentalists using the technology are vital. Many methodologies discard the quantitative results inherent in cdna microarray comparisons or cannot be flexibly applied to multifactorial experimental design. Here we present a flexible, quantitative Bayesian framework. This framework can be used to analyze normalized microarray data acquired by any replicated experimental design in which any number of treatments, genotypes, or developmental states are studied using a continuous chain of comparisons. Results: We apply this method to Saccharomyces cerevisiae microarray datasets on the transcriptional response to ethanol shock, to SNF2 and SWI1 deletion in rich and minimal media, and to wild-type and zap1 expression in media with high, medium, and low levels of zinc. The method is highly robust to missing data, and yields estimates of the magnitude of expression differences and experimental error variances on a per-gene basis. It reveals genes of interest that are differentially expressed at below the twofold level, genes with high 'fold-change' that are not statistically significantly different, and genes differentially regulated in quantitatively unanticipated ways. Conclusions: Anyone with replicated normalized cdna microarray ratio datasets can use the freely available MacOS and Windows software, which yields increased biological insight by taking advantage of replication to discern important changes in expression level both above and below a twofold threshold. Not only does the method have utility at the moment, but also, within the Bayesian framework, there will be considerable opportunity for future development.

关键词： cdna microarray Credible Interval SNF2 Expression Node cdna microarray data

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Identification of differentially expressed genes by meta-analysis of microarray data on breast cancer

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In Silico Biology 2008年第5-6期8卷 383-411页

作者： Kondrakhin, Yury V. Sharipov, Ruslan N. Kel, Alexander E. Kolpakov, Fedor A. Institute of Systems Biology Novosibirsk Russian Federation Design Technological Institute of Digital Techniques SB RAS Novosibirsk Russian Federation Institute of Cytology and Genetics SB RAS Novosibirsk Russian Federation BIOBASE GmbH Wolfenbüttel Germany

Albeit the great number of microarray data available on breast cancer, reliable identification of genes associated with breast cancer development remains a challenge. The aim of this work was to develop a novel method of meta-analysis for the identification of differentially expressed genes integrating results of several independent microarray experiments. We developed a statistical method for identification of up- and down-regulated genes to perform meta-analysis. The method takes advantage of hypergeometric and binomial distributions. Using our method we performed meta-analysis of five data sets from independent cdna-microarray experiments on breast cancer. The meta-analysis revealed that 3.2% and 2.8% of the 24,726 analyzed genes are significantly (P-value < 0.01) down- and up-regulated, respectively. We also show that properly applied meta-analysis is a good tool for comparison of different breast cancer subtypes. Our meta-analysis showed that the expression of the majority of genes does not show significant differences in different subtypes of breast cancer. Here, we report the rationale, development and application of meta-analysis that enable us to identify biologically meaningful features of breast cancer. The algorithm we propose for the meta-analysis can reveal the features specific to the breast cancer subtypes and those common to breast cancer. The results allow us to revise the previously generated lists of genes associated with breast cancer and also identify most promising anticancer drug-target genes. © 2008 IOS Press. All rights reserved.

关键词： Breast cancer cdna microarray data Cyclonet database Differentially expressed genes drug targets hypergeometric distribution Meta-analysis

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A meta-data based method for DNA microarray imputation

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BMC BIOINFORMATICS 2007年第1期8卷 109-109页

作者： Joernsten, Rebecka Ouyang, Ming Wang, Hui-Yu Univ Med & Dent New Jersey Inst Informat Piscataway NJ 08854 USA Rutgers State Univ Dept Stat New Brunswick NJ 08903 USA

Background: DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples under two or more conditions. Due to cost and design constraints of spotted cdna microarray experiments, each logical set commonly includes only a small number of replicates per condition. Despite the vast improvement of the microarray technology in recent years, missing values are prevalent. Intuitively, imputation of missing values is best done using many replicates within the same logical set. In practice, there are few replicates and thus reliable imputation within logical sets is difficult. However, it is in the case of few replicates that the presence of missing values, and how they are imputed, can have the most profound impact on the outcome of downstream analyses ( e. g. significance analysis and clustering). This study explores the feasibility of imputation across logical sets, using the vast amount of publicly available microarray data to improve imputation reliability in the small sample size setting. Results: We download all cdna microarray data of Saccharomyces cerevisiae, Arabidopsis thaliana, and Caenorhabditis elegans from the Stanford microarray database. Through cross-validation and simulation, we find that, for all three species, our proposed imputation using data from public databases is far superior to imputation within a logical set, sometimes to an astonishing degree. Furthermore, the imputation root mean square error for significant genes is generally a lot less than that of non-significant ones. Conclusion: Since downstream analysis of significant genes, such as clustering and network analysis, can be very sensitive to small perturbations of estimated gene effects, it is highly recommended that researchers apply reliable data imputation prior to further analysis. Our method can also be applied to cdna microarray experiments from other species, provided good reference d

关键词： Root Mean Square Error Imputation Accuracy Small Root Mean Square Error Stanford microarray database cdna microarray data

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Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

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BMC BIOINFORMATICS 2004年第1期5卷 60-60页

作者： Archer, KJ Dumur, CI Ramakrishnan, V Virginia Commonwealth Univ Dept Biostat Richmond VA 23298 USA Virginia Commonwealth Univ Ctr Study Biol Complex Richmond VA 23298 USA Virginia Commonwealth Univ Dept Pathol Richmond VA 23298 USA

Background: The usefulness of log(2) transformation for cdna microarray data has led to its widespread application to Affymetrix data. For Affymetrix data, where absolute intensities are indicative of number of transcripts, there is a systematic relationship between variance and magnitude of measurements. Application of the log(2) transformation expands the scale of genes with low intensities while compressing the scale of genes with higher intensities thus reversing the mean by variance relationship. The usefulness of these transformations needs to be examined. Results: Using an Affymetrix GeneChip(R) dataset, problems associated with applying the log(2) transformation to absolute intensity data are demonstrated. Use of the spread-versus-level plot to identify an appropriate variance stabilizing transformation is presented. For the data presented, the spread-versus-level plot identified a power transformation that successfully stabilized the variance of probe set summaries. Conclusion: The spread-versus-level plot is helpful to identify transformations for variance stabilization. This is robust against outliers and avoids assumption of models and maximizations.

关键词： Outlying Probe Affymetrix GeneChip Power Transformation cdna microarray data Expression Summary

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