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Clustering microarray data using model-based double K-means

JOURNAL OF APPLIED STATISTICS 2012年第9期39卷 1853-1869页

作者： Martella, Francesca Vichi, Maurizio Univ Roma La Sapienza Rome Italy

The microarray technology allows the measurement of expression levels of thousands of genes simultaneously. The dimension and complexity of gene expression data obtained by microarrays create challenging data analysis and management problems ranging from the analysis of images produced by microarray experiments to biological interpretation of results. Therefore, statistical and computational approaches are beginning to assume a substantial position within the molecular biology area. We consider the problem of simultaneously clustering genes and tissue samples (in general conditions) of a microarray data set. This can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. The need of finding a subset of genes and tissue samples defining a homogeneous block had led to the application of double clustering techniques on gene expression data. Here, we focus on an extension of standard K-means to simultaneously cluster observations and features of a data matrix, namely double K-means introduced by Vichi (2000). We introduce this model in a probabilistic framework and discuss the advantages of using this approach. We also develop a coordinate ascent algorithm and test its performance via simulation studies and real data set. Finally, we validate the results obtained on the real data set by building resampling confidence intervals for block centroids.

关键词： microarray data model-based biclustering double K-means stratified resampling procedure coordinate ascent algorithm

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ExpressYourself: a modular platform for processing and visualizing microarray data

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NUCLEIC ACIDS RESEARCH 2003年第13期31卷 3477-3482页

作者： Luscombe, NM Royce, TE Bertone, P Echols, N Horak, CE Chang, JT Snyder, M Gerstein, M Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06520 USA Yale Univ Dept Stat New Haven CT 06520 USA

DNA microarrays are widely used in biological research;by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed;for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://***/expressyourself.

关键词： .slide microarray data user interface Introduction data ExpressYourself platform for processing data DOWNLOAD CONCLUSIONS AVAILABILITY REFERENCES

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A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

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GENOMICS 2016年第6期107卷 231-238页

作者： Sharbaf, Fatemeh Vafaee Mosafer, Sara Moattar, Mohammad Hossein Imam Reza Int Univ Dept Comp Engn Mashhad Iran Islamic Azad Univ Dept Software Engn Mashhad Branch Mashhad Iran

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor;support vector machine and naive Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Gene selection microarray data Cellular learning automata Ant colony optimization K-nearest neighbor Naive Bayes

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Gene selection and classification of microarray data: A Pareto DE approach

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2017年第1期11卷 93-107页

作者： Dash, Rasmita Misra, Bijan Siksha O Anusandhan Univ Dept Comp Sci & Informat Technol Bhubaneswar 751030 Odisha India Silicon Inst Technol Dept Comp Sci & Engn Bhubaneswar 751024 Odisha India

Sample classification is a most critical task in microarray data analysis. But representation of microarray data with the huge search space of thousands of gene makes this work more complex and difficult. To handle this problem both an efficient gene selection technique and efficient classifier is required. In this paper, we propose a multi-criterion Pareto differential evolution technique for feature selection. This technique first uses a wrapper technique i.e. a population based differential evolution gene selection (DEGS) algorithm for feature selection. The motivation of choosing differential evolution as compared to other learning technique is it tries to assign optimal ranks to each gene using probability distribution factor present in the microarray dataset using classification error as the fitness function. It is observed that these selections contain relevant genes as well as some irrelevant genes. So in the second phase bi-objective filter technique, called as Pareto based optimization is used to select minimum number of top ranked genes in the feature selection. Here we have considered information gain (IG) and Signal to noise ratio (SNR) as two objective functions for Pareto optimization. To verify the importance and relevance of the selected genes, classification using K-nearest neighbour (KNN), naive Bayesian classifier (NB), artificial neural network (ANN) and support vector machine (SVM) is done. Our experiment is conducted over four well known microarray dataset. The experimental work shows that the proposed method is better than the existing searching method in terms of both classification error and predicted feature sets. The classification result shows that performance of SVM classifier is better than the result obtained using KNN, NB and ANN. Finally this method highlights its performance in terms of both relevance of genes and classification output.

关键词： Feature selection microarray data filter method wrapper method differential evolution pareto optimal solution

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Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

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TOXICOLOGY AND APPLIED PHARMACOLOGY 2013年第3期271卷 386-394页

作者： Ovacik, Meric A. Sen, Banalata Euling, Susan Y. Gaido, Kevin W. Ierapetritou, Marianthi G. Androulakis, Ioannis P. Rutgers State Univ Chem & Biochem Engn Dept Piscataway NJ 08854 USA US EPA Natl Ctr Environm Assessment Res Triangle Pk NC 27709 USA US EPA Natl Ctr Environm Assessment Off Res & Dev Washington DC 20460 USA US FDA Ctr Vet Med Off New Anim Drug Evaluat Div Human Food Safety Rockville MD 20855 USA Rutgers State Univ Dept Biomed Engn Piscataway NJ 08854 USA

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data. (C) 2010 Published by Elsevier Inc.

关键词： Dibutyl Phthalate (DBP) microarray data Pathway analysis Singular value decomposition (SVD) Time course

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Graph clustering-based discretization approach to microarray data

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KNOWLEDGE AND INFORMATION SYSTEMS 2019年第2期60卷 879-906页

作者： Sriwanna, Kittakorn Boongoen, Tossapon Iam-On, Natthakan Chiang Rai Rajabhat Univ Sch Comp & Informat Technol Muang 57100 Chiang Rai Thailand Mae Fah Luang Univ Sch Informat Technol IQ D Res Unit Muang 57100 Chiang Rai Thailand

Several techniques in data mining require discrete data. In fact, learning with discrete domains often performs better than the case of continuous data. Multivariate discretization is the algorithm that transforms continuous data to discrete one by considering correlations among attributes. Given the benefit of this idea, many multivariate discretization algorithms have been proposed. However, there are a few discretization algorithms that directly apply to microarray or gene expression data, which is high-dimensional and unbalance data. Even so interesting, no multivariate method has been put forward for microarray data analysis. According to the recent published research, graph clustering-based discretization of splitting and merging methods (GraphS and GraphM) usually achieves superior results compared to many well-known discretization algorithms. In this paper, GraphS and GraphM are extended by adding the alpha parameter that is the ratio between the similarity of gene expressions (distance) and the similarity of the class label. Moreover, the extensions consider 3 similarity measures of cosine similarity, Euclidean distance, and Pearson correlation in order to determine the proper pairwise similarity measure. The evaluation against 20 real microarray datasets and 4 classifiers suggests that the results of three classification performances (ACC, AUC, Kappa) and running time of two proposed methods based on cosine similarity, GraphM(C) and GraphS(C) are better than 9 state-of-the-art discretization algorithms.

关键词： Multivariate discretization Graph clustering microarray data High-dimensional data data mining

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Selecting informative genes from microarray data by using hybrid methods for cancer classification

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ARTIFICIAL LIFE AND ROBOTICS 2009年第2期13卷 414-417页

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Misman, Muhammad Faiz Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai Johore Malaysia

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.

关键词： Cancer classification Genetic algorithm Gene selection Hybrid method microarray data

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Analysis of time-course microarray data: Comparison of common tools

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GENOMICS 2019年第4期111卷 636-641页

作者： Moradzadeh, Kobra Moein, Shiva Nickaeen, Niloofar Gheisari, Yousof Isfahan Univ Med Sci Dept Genet & Mol Biol Esfahan *** Iran Isfahan Univ Med Sci Sch Med Student Res Comm Esfahan Iran Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Isfahan Univ Med Sci Isfahan Kidney Dis Res Ctr Regenerat Med Lab Esfahan Iran

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

关键词： Gene expression profiling microarray data Time-series analysis

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Evaluation of artificial time series microarray data for dynamic gene regulatory network inference

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JOURNAL OF THEORETICAL BIOLOGY 2017年 426卷 1-16页

作者： Xenitidis, P. Seimenis, I. Kakolyris, S. Adamopoulos, A. Democritus Univ Thrace Sch Med Med Phys Lab Alexandroupolis Greece Democritus Univ Thrace Sch Med Dept Med Oncol Alexandroupolis Greece

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time ddmain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. (C) 2017 Elsevier Ltd. All

关键词： Gene regulatory networks Inference microarray data

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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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