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Determination of cluster number in clustering microarray data

APPLIED MATHEMATICS AND COMPUTATION 2005年第2期169卷 1172-1185页

作者： Shen, JD Chang, SI Lee, ES Deng, YP Brown, SJ Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA Kansas State Univ Div Biol Bioinformat Program Manhattan KS 66506 USA

The general purpose of clustering analysis of microarray data is to organize the data into meaningful groups based on their closeness. Although various algorithms have been proposed for the clustering of microarray data, the main difficulty remains to be the determination of the optimal number of clusters. To complicate the problem further, meaningful groups or closeness cannot be well defined due to the fuzziness nature of the data. This paper proposes a dynamic validity index to overcome this problem. The proposed index, in addition of the dynamic aspects, also takes care of both the intra- and the inter-distances of the clusters. An algorithm based on the proposed dynamic validity index and the traditional K-means method was developed. To make the proposed dynamic validity index more flexible, a modulating parameter gamma is introduced. This parameter can be used to take care of noisy data and balance the importance between compactness and separateness in the clusters. To illustrate the effectiveness of the approach, a numerical example by using the human serum data from the literature was solved and the sensitivity and robustness of the approach are examined. (c) 2004 Elsevier Inc. All rights reserved.

关键词： clustering microarray data K-means algorithm data mining validity index bio-informatics

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A Novel SVM-RFE for Gene Selection

A Novel SVM-RFE for Gene Selection

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第三届最优化与系统生物学国际研讨会

作者： Jun-Yan Tan Zhi-Xia Yang Naiyang Deng College of Science China Agricultural University College of Mathematics and Systems Science Xinjiang University Academy of Mathematics and Systems Science CAS

Selecting a subset of informative genes from microarray expression data is a critical data preparation step in cancer classification and other biological function *** support vector machine recursive feature elimination(SVM-RFE) is one of the most effective feature selection method which has been successfully used in selecting informative genes for cancer classification. While,the SVM-RFE selects genes only using the gene expression data without using any other biological information of the *** on the biology information of the genes,it may be beneficial to identify the genes that are relevant to the *** propose a novel SVM-RFE method for gene selection by incorporating the Kyoto Encyclopedia of genes and genomes(KEGG) pathway information into feature selection *** results indicate that the novel SVM-RFE tends to provide better variable selection results than the SVM-RFE.

关键词： Support vector machine microarray data gene selection

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Novel techniques for microarray data analysis: Probabilistic Principal Surfaces and Competitive Evolution on data

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2005年第4期2卷 514-523页

作者： Amato, R Ciaramella, A Del Mondo, C De Vinco, L Donalek, C Longo, G Miele, G Raiconi, G Staiano, A Tagliaferri, R Univ Salerno Dept Math & Informat Fisciano Sa Italy Univ Naples Federico II Dept Phys Sci Naples Italy Univ Naples Federico II Dept Math & Applicat Naples Italy Ist Nazl Fis Nucl Italian Inst Nucl Phys Unit Naples I-80125 Naples Italy INAF Italian Inst Astrophys Naples Italy

microarrays are among the most powerful tools in biological research, but in order to attain its full potentialities, it is imperative to develop techniques capable to effectively exploit the huge quantity of data which they produce. In this paper two machine learning methodologies for microarray data analysis are proposed: (1) Probabilistic Principal Surfaces (PPS), which is a nonlinear latent variable model which offers very appealing visualization and classification abilities and can be effectively employed for clustering purposes. More specifically, the PPS method builds a probability density function of a given data set of patterns, lying in a D-dimensional space (with D >> 3), expressed in terms of a fixed number of latent variables, lying in a Q-dimensional space (Q is usually 2 or 3), which can be used (after a proper manipulation) to visualize, classify and cluster the data;(2) Competitive Evolution on data (CED) is instead an evolutionary system in which the possible solutions (cluster centroids) compete to conquer the largest possible number of resources (data) and thus partition the input data set in clusters. We discuss the application of both methods to the analysis of microarray data obtained for the yeast genome.

关键词： microarray data data mining latent variable models evolutionary algorithms visualization clustering

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MicroCluster: Efficient deterministic biclustering of microarray data

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IEEE INTELLIGENT SYSTEMS 2005年第6期20卷 40-+页

作者： Zhao, LZ Zaki, MJ Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. Our approach has four key features. First, we mine only the maximal biclusters satisfying certain homogeneity criteria. Second, the clusters can be arbitrarily positioned anywhere in the input data matrix, and they can have arbitrary overlapping regions. Third, MicroCluster uses a flexible definition of a cluster that lets it mine several types of biclusters (which previously were studied independently). Finally, MicroCluster can delete or merge biclusters that have large overlaps. So, it can tolerate some noise in the data set and let users focus on the most important clusters. We\'ve developed a set of metrics to evaluate the clustering quality and have tested MicroCluster\'s effectiveness on several synthetic and real data sets.

关键词： biology computing data mining genetics pattern clustering MicroCluster deterministic biclustering genetic data mining microarray data bicluster bioinformatics clustering gene expression microarrays microarray Genetics Bioinformatics data mining dataset Cluster Analysis biology computing

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Fuzzy rule based unsupervised approach for gene saliency

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BMC BIOINFORMATICS 2009年第Sup7期10卷 1-1页

作者： Verma, Nishchal K. Agrawal, Pooja Cui, Yan Univ Tennessee Ctr Integrat & Translat Gen Dept Mol Sci Memphis TN 38163 USA

An abstract of a study related to unsupervised approach on gene saliency based on the fuzzy rule, which was conducted by Nishchal K. Verma, Pooja Agrawal, and Yan Cui, is presented.

关键词： microarray data Fuzzy Rule Classifier Performance Ranking Method Class Information

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Evaluation of gene importance in microarray data based upon probability of selection

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BMC BIOINFORMATICS 2005年第1期6卷 1-11页

作者： Fu, LM Fu-Liu, CS Pacific TB & Canc Res Org Pasadena CA USA Univ Florida Gainesville FL USA

Background: microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty. Results: Here we present a model for probability analysis of selected genes in order to determine their importance. Our contribution is that we show how to derive the P value of each selected gene in multiple gene selection trials based on different combinations of data samples and how to conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P value in that a smaller value implies higher information content from information theory. On the microarray data concerning the subtype classification of small round blue cell tumors, we demonstrate that the method is capable of finding the smallest set of genes ( 19 genes) with optimal classification performance, compared with results reported in the literature. Conclusion: In classifier design based on microarray data, the probability value derived from gene selection based on multiple combinations of data samples enables an effective mechanism for reducing the tendency of fitting local data particularities.

关键词： Support Vector Machine microarray data Support Vector Machine Classifier Gene Selection Subtype Classification

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Gene selection for microarray data analysis using principal component analysis

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STATISTICS IN MEDICINE 2005年第13期24卷 2069-2087页

作者： Wang, AT Gehan, EA Georgetown Univ Vincent T Lombardi Canc Res Ctr Dept Biomath & Biostat Washington DC USA

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： microarray data principal component analysis procrustes criterion gene selection

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MIDAW: a web tool for statistical analysis of microarray data

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NUCLEIC ACIDS RESEARCH 2005年第Web Server issue期33卷 W644-W649页

作者： Romualdi, C Vitulo, N Favero, MD Lanfranchi, G Univ Padua Dipartimento Biol CRIBI Biotechnol Ctr I-35121 Padua Italy

MIDAW (microarray data analysis web tool) is a web interface integrating a series of statistical algorithms that can be used for processing and interpretation of microarray data. MIDAW consists of two main sections: data normalization and data analysis. In the normalization phase the simultaneous processing of several experiments with background correction, global and local mean and variance normalization are carried out. The data analysis section allows graphical display of expression data for descriptive purposes, estimation of missing values, reduction of data dimension, discriminant analysis and identification of marker genes. The statistical results are organized in dynamic web pages and tables, where the transcript/ gene probes contained in a specific microarray platform can be linked ( according to user choice) to external databases (GenBank, Entrez Gene, UniGene). Tutorial files help the user throughout the statistical analysis to ensure that the forms are filled out correctly. MIDAW has been developed using Perl and PHP and it uses R/Bioconductor languages and routines. MIDAW is GPL licensed and freely accessible at http://***. ***/midaw/.Perl and PHP source codes are available from the authors upon request.

关键词： .discriminant data analysis normalization Statistical analysis microarray data expression data local mean missing values web tool MIDAW

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Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data

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BMC BIOINFORMATICS 2005年第1期6卷 1-9页

作者： Jain, N Cho, HJ O'Connell, M Lee, JK Univ Virginia Sch Med Dept Hlth Evaluat Sci Div Biostat & Epidemiol Charlottesville VA 22908 USA Insightful Corp Durham NC 27713 USA

Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates. Results: We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data. Conclusion: We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.

关键词： False Discovery Rate microarray data Null Distribution microarray data Analysis Null data

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Dimension reduction-based penalized logistic regression for cancer classification using microarray data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005年第2期2卷 166-175页

作者： Shen, L Tan, EC Nanyang Technol Univ BioInformat Res Ctr Singapore 637553 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.

关键词： dimension reduction penalized logistic regression singular value decomposition partial least squares cancer classification classifier design and evaluation feature evaluation and selection microarray data

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