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Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

BMC BIOINFORMATICS 2009年第1期10卷 354-354页

作者： Chatziioannou, Aristotelis Moulos, Panagiotis Kolisis, Fragiskos N. Natl Hellen Res Fdn Inst Biol Res & Biotechnol Metab Engn & Bioinformat Grp Athens 11635 Greece

Background: The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. Results: We describe here Gene ARMADA ( Automated Robust microarray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form ( Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Concl

关键词： microarray dataset microarray Data Analysis Affymetrix Array Data Analysis Structure Bioinformatics Toolbox

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A unified framework for finding differentially expressed genes from microarray experiments

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BMC BIOINFORMATICS 2007年第1期8卷 1-21页

作者： Shaik, Jahangheer S. Yeasin, Mohammed Univ Memphis Dept Elect & Comp Engn CVPIA Lab Memphis TN 38152 USA Univ Memphis Bioinformat Program CVPIA Lab Memphis TN 38152 USA Univ Memphis CVPIA Lab Memphis TN 38152 USA Univ Memphis Ctr Adv Robot CVPIA Lab Memphis TN 38152 USA Univ Memphis Software Testing & Excellence Program Memphis TN 38152 USA

Background: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. Results: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. Conclusion: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experi

关键词： Acute Myeloid Leukemia Gene Selection Unify Framework microarray dataset Acute Lymphoid Leukemia

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Spotting effect in microarray experiments

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BMC BIOINFORMATICS 2004年第1期5卷 63-63页

作者： Mary-Huard, T Daudin, JJ Robin, S Bitton, F Cabannes, E Hilson, P Inst Natl Agron Paris Grignon F-75231 Paris France Univ Evry CNRS INRA UMR Gen Vegetale F-91057 Evry France Inst Pasteur Lab Immunol Virale F-75724 Paris France State Univ Ghent VIB Dept Plant Syst Biol B-9052 Ghent Belgium

Background: microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array. Results: Using the variogram, a geostatistical tool, we characterized the observed variability, called here the spotting effect because it most probably arises during steps in the array printing procedure. Conclusions: The spotting effect is not appropriately corrected by current normalization methods, even by those addressing spatial variability. Importantly, the spotting effect may alter differential and clustering analysis.

关键词： microarray Experiment Normalization Procedure microarray dataset Periodic Pattern Spatial Bias

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Discovering time-lagged rules from microarray data using gene profile classifiers

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BMC BIOINFORMATICS 2011年第1期12卷 1-21页

作者： Gallo, Cristian A. Carballido, Jessica A. Ponzoni, Ignacio Univ Nacl Sur Dept Ciencias & Ingn Computac Lab Invest & Desarrollo Computac Cient LIDeCC RA-8000 Bahia Blanca Buenos Aires Argentina UNS CONICET Planta Piloto Ingn Quim PLAPIQUI Bahia Blanca Buenos Aires Argentina

Background: Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time delayed gene regulatory networks that govern the majority of these molecular processes. Results: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions: A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

关键词： Association Rule microarray dataset Inference Process Regulation Threshold Time Series dataset

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Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

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BMC BIOINFORMATICS 2010年第1期11卷 567-567页

作者： Bartenhagen, Christoph Klein, Hans-Ulrich Ruckert, Christian Jiang, Xiaoyi Dugas, Martin Univ Munster Dept Med Informat & Biomath D-48149 Munster Germany Univ Munster Dept Comp Sci D-48149 Munster Germany

Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data. Results: A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods. Conclusions: Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.

关键词： Principal Component Analysis Support Vector Machine Target Space microarray dataset Target Dimension

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A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

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BMC BIOINFORMATICS 2010年第Sup1期11卷 S5-S5页

作者： Yang, Pengyi Zhou, Bing B. Zhang, Zili Zomaya, Albert Y. Univ Sydney Sch Informat Technol J12 Sydney NSW 2006 Australia NICTA Eveleigh NSW 2015 Australia Southwest Univ Fac Comp & Informat Sci Cq 400715 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia Univ Sydney Sydney Bioinformat Sydney NSW 2006 Australia Univ Sydney Ctr Math Biol Sydney NSW 2006 Australia Univ Sydney Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses. Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system. Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

关键词： Feature Selection Majority Vote Gene Selection microarray dataset Gene Subset

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A balanced iterative random forest for gene selection from microarray data

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BMC BIOINFORMATICS 2013年第1期14卷 1-10页

作者： Anaissi, Ali Kennedy, Paul J. Goyal, Madhu Catchpoole, Daniel R. Univ Technol Sydney Fac Engn & Informat Technol Ctr Quantum Computat & Intelligent Syst QCIS Broadway NSW 2007 Australia Childrens Hosp Westmead Childrens Canc Res Unit Tumour Bank Westmead NSW 2145 Australia

Background: The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease. Results: This paper introduces a Balanced Iterative Random Forest (BIRF) algorithm to select the most relevant genes for a disease from imbalanced high-throughput gene expression microarray data. Balanced iterative random forest is applied on four cancer microarray datasets: a childhood leukaemia dataset, which represents the main target of this paper, collected from The Children's Hospital at Westmead, NCI 60, a Colon dataset and a Lung cancer dataset. The results obtained by BIRF are compared to those of Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Multi-class SVM-RFE (MSVM-RFE), Random Forest (RF) and Naive Bayes (NB) classifiers. The results of the BIRF approach outperform these state-of-the-art methods, especially in the case of imbalanced datasets. Experiments on the childhood leukaemia dataset show that a 7%similar to 12% better accuracy is achieved by BIRF over MSVM-RFE with the ability to predict patients in the minor class. The informative biomarkers selected by the BIRF algorithm were validated by repeating training experiments three times to see whether they are globally informative, or just selected by chance. The results show that 64% of the top genes consistently appear in the three lists, and the top 20 genes remain near the top in the other three lists. Conclusion: The designed BIRF algorithm is an appropriate choice to select genes from imbalanced high-throughput gene expression microarray data. BIRF outperforms the state-of-the-art methods, especially the ability to handle the class-imbalanced data. Moreover, the an

关键词： Support Vector Machine Random Forest Malignant Pleural Mesothelioma Acute Lymphoblastic Leukaemia microarray dataset

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AMDA: an R package for the automated microarray data analysis

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BMC BIOINFORMATICS 2006年第1期7卷 335-335页

作者： Pelizzola, Mattia Pavelka, Norman Foti, Maria Ricciardi-Castagnoli, Paola Department of Biotechnology and Biosciences University of Milano-Bicocca Piazza della Scienza 2 20126 Milan Italy

\Background: microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. Results: To address these problems we have developed an automated microarray data analysis ( AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. Conclusion: AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://***/

关键词： Differentially Express Gene microarray dataset Open Source Software Project Full Concordance Silhouette Score

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GeneXplorer: an interactive web application for microarray data visualization and analysis

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BMC BIOINFORMATICS 2004年第1期5卷 141-141页

作者： Rees, CA Demeter, J Matese, JC Botstein, D Sherlock, G Stanford Univ Sch Med Dept Genet Stanford CA 94305 USA Stanford Univ Sch Med Dept Biochem Stanford CA 94305 USA Princeton Univ Lewis Sigler Inst Integrat Genom Carl Icahn Lab Princeton NJ 08544 USA

Background: When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. Results: We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. Conclusions: The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://***/dist/microarray-GeneXplorer/.

关键词： Application Programming Interface microarray dataset External Database Stanford microarray Database Cluster Gene Expression Data

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Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional microarray Data

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PROCESSES 2023年第2期11卷 562页

作者： Ali, Waleed Saeed, Faisal King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Informat Technol Dept Jeddah 25729 Saudi Arabia Birmingham City Univ Sch Comp & Digital Technol Dept Comp & Data Sci DAAI Res Grp Birmingham B4 7XG England

The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method's capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study-this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.

关键词： cancer classification filter feature selection genetic algorithm gene selection microarray dataset

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