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MicroGen: a MIAME compliant web system for microarray experiment information and workflow management

BMC BIOINFORMATICS 2005年第4-Sup期6卷 1-6页

作者： Burgarella, S Cattaneo, D Pinciroli, F Masseroli, M Politecn Milan Dept Bioengn Biomed Informat Lab I-20133 Milan Italy

Background: Improvements of bio-nano-technologies and biomolecular techniques have led to increasing production of high-throughput experimental data. Spotted cDNA microarray is one of the most diffuse technologies, used in single research laboratories and in biotechnology service facilities. Although they are routinely performed, spotted microarray experiments are complex procedures entailing several experimental steps and actors with different technical skills and roles. During an experiment, involved actors, who can also be located in a distance, need to access and share specific experiment information according to their roles. Furthermore, complete information describing all experimental steps must be orderly collected to allow subsequent correct interpretation of experimental results. Results: We developed MicroGen, a web system for managing information and workflow in the production pipeline of spotted microarray experiments. It is constituted of a core multi-database system able to store all data completely characterizing different spotted microarray experiments according to the Minimum Information About microarray experiments (MIAME) standard, and of an intuitive and user-friendly web interface able to support the collaborative work required among multidisciplinary actors and roles involved in spotted microarray experiment production. MicroGen supports six types of user roles: the researcher who designs and requests the experiment, the spotting operator, the hybridisation operator, the image processing operator, the system administrator, and the generic public user who can access the unrestricted part of the system to get information about MicroGen services. Conclusion: MicroGen represents a MIAME compliant information system that enables managing workflow and supporting collaborative work in spotted microarray experiment production.

关键词： microarray experiment Collaborative Work Hybridisation Operator experimental Step Database Table

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A web-based microarray experiment management system 15

A web-based microarray experiment management system

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15th International Conference on Computer Applications in Industry and Engineering (CAINE-2002)

作者： Lu, ML Xiong, Y Zhang, D Calif State Univ Sacramento Dept Comp Sci Sacramento CA 95819 USA

ISBN: (纸本)1880843455

microarray technology provides unprecedented powerful insights into gene expression;and it can soon be expected to do the same for protein expression. However, a low cost and practical tool for microarray experiment management is still hard to find these days. This paper presents a web-based microarray experiment management system that we developed for UC Davis Cancer Research Center. The system greatly improves the accessibility of the microarray facility and therefore speeds up functional genomics research. Our system configuration involves four open source components: Apache, PHP, MySQL, and Linux. Two groups of functions with authentication are provided in the system using a web browser: one for the administrator and one for users. The system is an adaptive, efficient, and expandable bioinformatics tool.

关键词： microarray experiment PHP MySQL

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Comparison of Similarity Metrics in microarray experiment Retrieval 23

Comparison of Similarity Metrics in Microarray Experiment Re...

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23nd Signal Processing and Communications Applications Conference (SIU)

作者： Acici, Koray Ogul, Hasan Baskent Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781467373869

Content-based retrieval of biological experiments is a recent challenge in bioinformatics. The task is to search in a database using a query-by-example without any meta-data annotation. In this study, for retrieving relevant microRNA experiments from microarray repositories, performance evaluation of known similarity metrics was conducted to compare experiment fingerprints. It was shown that Spearman correlation coefficient outperformed others by comparison on real datasets. This result shows that ranks of fingerprint values are more important than the exact values in experiment fingerprint.

关键词： microRNA information retrieval microarray experiment

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Algorithmic construction of optimal block designs for two-colour cDNA microarray experiments using the linear mixed effects model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019年第7期48卷 1948-1963页

作者： Debusho, Legesse K. Gemechu, Dibaba B. Haines, Linda M. Univ South Africa Dept Stat Sci CampusGJ Gerwel C BlockFloor 6Florida Pk ZA-1710 Roodepoort Florida South Africa Univ Cape Town Dept Stat Sci Rondebosch South Africa

In this study, methods for efficient construction of A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments with array as the block effect are considered. Two algorithms, namely the array exchange and treatment exchange algorithms together with the complete enumeration technique are introduced. For large numbers of arrays or treatments or both, the complete enumeration method is highly computer intensive. The treatment exchange algorithm computes the optimal or near-optimal designs faster than the array exchange algorithm. The two methods however produce optimal or near-optimal designs with the same efficiency under the four optimality criteria.

关键词： Array exchange algorithm A-, MV-, D- and E-optimal designs Complete enumeration microarray experiment Treatment exchange algorithm

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From co-expression to co-regulation: how many microarray experiments do we need?

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GENOME BIOLOGY 2004年第7期5卷 1-11页

作者： Yeung, KY Medvedovic, M Bumgarner, RE Univ Washington Dept Microbiol Seattle WA 98195 USA Univ Cincinnati Med Ctr Dept Environm Hlth Ctr Genome Informat Cincinnati OH 45267 USA

Background: Cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance. Results: We applied various clustering algorithms to microarray datasets with different sizes, and we evaluated the clustering results by determining the fraction of gene pairs from the same clusters that share at least one known common transcription factor. We used both yeast transcription factor databases (SCPD, YPD) and chromatin immunoprecipitation (ChIP) data to evaluate our clustering results. We showed that the ability to identify co-regulated genes from clustering results is strongly dependent on the number of microarray experiments used in cluster analysis and the accuracy of these associations plateaus at between 50 and 100 experiments on yeast data. Moreover, the model-based clustering algorithm MCLUST consistently outperforms more traditional methods in accurately assigning co-regulated genes to the same clusters on standardized data. Conclusions: Our results are consistent with respect to independent evaluation criteria that strengthen our confidence in our results. However, when one compares ChIP data to YPD, the false-negative rate is approximately 80% using the recommended p-value of 0.001. In addition, we showed that even with large numbers of experiments, the false-positive rate may exceed the true-positive rate. In particular, even when all experiments are included, the best results produce clusters with only a 28% true-positive rate using known gene transcription factor interactions.

关键词： Cluster Algorithm microarray experiment Additional Data File microarray Dataset Gene Subset

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A Laplace mixture model for identification of differential expression in microarray experiments

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BIOSTATISTICS 2006年第4期7卷 630-641页

作者： Bhowmick, Debjani Davison, A. C. Goldstein, Darlene R. Ruffieux, Yann Ecole Polytech Fed Lausanne Inst Math EPFL FSB IMA Stn 8 CH-1015 Lausanne Switzerland

microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.

关键词： Laplace distribution marginal likelihood microarray experiment mixture model REML

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DESIGNS WITH BLOCKS OF SIZE TWO AND APPLICATIONS TO microarray experimentS

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ANNALS OF STATISTICS 2018年第6期46卷 2775-2805页

作者： Godolphin, Janet Univ Surrey Dept Math Guildford GU2 7XH Surrey England

Designs with blocks of size two have numerous applications. In experimental situations where observation loss is common, it is important for a design to be robust against breakdown. For designs with one treatment factor and a single blocking factor, with blocks of size two, conditions for connectivity and robustness are obtained using combinatorial arguments and results from graph theory. Lower bounds are given for the breakdown number in terms of design parameters. For designs with equal or near equal treatment replication, the concepts of treatment and block partitions, and of linking blocks, are used to obtain information on the number of blocks required to guarantee various levels of robustness. The results provide guidance for construction of designs with good robustness properties. Robustness conditions are also established for row column designs in which one of the blocking factors involves blocks of size two. Such designs are particularly relevant for microarray experiments, where the high risk of observation loss makes robustness important. Disconnectivity in row column designs can be classified as three types. Techniques are given to assess design robustness according to each type, leading to lower bounds for the breakdown number. Guidance is given for robust design construction. Cyclic designs and interwoven loop designs are shown to have good robustness properties.

关键词： Breakdown number connectivity incomplete block design microarray experiment observation loss robustness row column design

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Designs for two-colour microarray experiments

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2007年第4期56卷 365-394页

作者： Bailey, R. A. Univ London Sch Math Sci London England

Designs for two-colour microarray experiments can be viewed as block designs with two treatments per block. Explicit formulae for the A- and D-criteria are given for the case that the number of blocks is equal to the number of treatments. These show that the A- and D-optimality criteria conflict badly if there are 10 or more treatments. A similar analysis shows that designs with one or two extra blocks perform very much better, but again there is a conflict between the two optimality criteria for moderately large numbers of treatments. It is shown that this problem can be avoided by slightly increasing the number of blocks. The two colours that are used in each block effectively turn the block design into a row-column design. There is no need to use a design in which every treatment has each colour equally often: rather, an efficient row-column design should be used. For odd replication, it is recommended that the row-column design should be based on a bipartite graph, and it is proved that the optimal such design corresponds to an optimal block design for half the number of treatments. Efficient row-column designs are given for replications 3-6. It is shown how to adapt them for experiments in which some treatments have replication only 2.

关键词： block design design of experiments graph theory microarray experiment optimal design robustness row-column design

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A Bayesian determination of threshold for identifying differentially expressed genes in microarray experiments

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STATISTICS IN MEDICINE 2006年第18期25卷 3174-3189页

作者： Chen, Jie Sarkar, Sanat K. Temple Univ Dept Stat Philadelphia PA 19422 USA Merck Res Labs West Point PA 19486 USA

The original definitions of false discovery rate (FDR) and false non-discovery rate (FNR) can be understood as the frequentist risks of false rejections and false non-rejections, respectively, conditional on the unknown parameter, while the Bayesian posterior FDR and posterior FNR are conditioned on the data. From a Bayesian point of view, it seems natural to take into account the uncertainties in both the parameter and the data. In this spirit, we propose averaging out the frequentist risks of false rejections and false non-rejections with respect to some prior distribution of the parameters to obtain the average FDR (AFDR) and average FNR (AFNR), respectively. A linear combination of the AFDR and AFNR, called the average Bayes error rate (ABER), is considered as an overall risk. Some useful formulas for the AFDR, AFNR and ABER are developed for normal samples with hierarchical mixture priors. The idea of finding threshold values by minimizing the ABER or controlling the AFDR is illustrated using a gene expression data set. Simulation studies show that the proposed approaches are more powerful and robust than the widely used FDR method. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： average false discovery rate average false non-discovery rate average Bayes error rate hierarchical mixture model microarray experiment

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Visualization of large microarray experiments with space maps

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BMC BIOINFORMATICS 2009年第s13期10卷 O7-O7页

作者： Gehlenborg, Nils Brazma, Alvis European Bioinformat Inst Cambridge CB10 1SD England Univ Cambridge Grad Sch Life Sci Cambridge CB2 1RX England

The article focuses on the use of Space Maps visualization technique for huge microarray experiments. It notes that the Space Maps visualization technique is an approach to visualization of gene expression data that facilitates the visualization of expression profiles of genes with many samples without loss of context information. The authors say that the major strength of the said approach is that it enables a tightly coupled exploration of local and global patterns, which makes hypothesis generation more efficient compared to traditional techniques.

关键词： Gene Expression Profile Gene Expression Data Context Information microarray experiment Local Pattern

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