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Difference-based clustering of short time-course microarray data with replicates

BMC BIOINFORMATICS 2007年第1期8卷 253-253页

作者： Kim, Jihoon Kim, Ju Han Seoul Natl Univ Coll Med Seoul 110799 South Korea Univ Wisconsin Med Sci Ctr Dept Stat Madison WI 53706 USA

Background: There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically. Results: We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods. Conclusions: Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.

关键词： Gene Ontology cluster algorithm cluster Result cluster Number True cluster

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Evaluation of gene-expression clustering via mutual information distance measure

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BMC BIOINFORMATICS 2007年第1期8卷 111-111页

作者： Priness, Ido Maimon, Oded Ben-Gal, Irad Tel Aviv Univ Dept Ind Engn IL-69978 Tel Aviv Israel

Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information ( MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient. Results: Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions. It was found that the use of the MI measure yields a more significant differentiation among erroneous clustering solutions. The proposed measure was also used to analyze the performance of several known clustering algorithms. A comparative study of these algorithms reveals that their "best solutions" are ranked almost oppositely when using different distance measures, despite the found correspondence between these measures when analysing the averaged scores of groups of solutions. Conclusion: In view of the results, further attention should be paid to the selection of a proper distance measure for analyzing the clustering of gene expression data.

关键词： Euclidean Distance cluster algorithm Similarity Measure Mutual Information Distance Measure

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Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

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BMC BIOINFORMATICS 2007年第1期8卷 250-250页

作者： Huttenhower, Curtis Flamholz, Avi I. Landis, Jessica N. Sahi, Sauhard Myers, Chad L. Olszewski, Kellen L. Hibbs, Matthew A. Siemers, Nathan O. Troyanskaya, Olga G. Coller, Hilary A. Princeton Univ Dept Mol Biol Princeton NJ 08544 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Bristol Myers Squibb Co Pennington NJ 08534 USA

Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks;these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes ( e. g. ribosomes). Results: We developed Nearest Neighbor Networks ( NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large data

关键词： cluster algorithm Gene Pair Neighborhood Size Random Data Neighbor Network

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Super paramagnetic clustering of DNA sequences

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JOURNAL OF BIOLOGICAL PHYSICS 2006年第1期32卷 11-25页

作者： Radjiman, S Han, LY Wang, JS Chen, YZ Natl Univ Singapore Dept Computat Sci Singapore 117543 Singapore

An unsupervised clustering of 4541 DNA sequences containing active promoter regions from vertebrate and arthropod classes (including their viral genes) was performed. All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.

关键词： DNA sequence promoters transcription factor binding sites cluster algorithm data clustering statistical physics

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Research on virtual logistic enterprises partners selection method based on CLIQUE

Research on virtual logistic enterprises partners selection ...

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10th International Conference on Computer Supported Cooperative Work in Design

作者： Xu, Song Shen, Lixin Zhai, Jun Yang, Zan Li, Yongxian Dalian Maritime Univ Econ & Management Coll Liaoning 116026 Peoples R China Liaoning Normal Univ Liaoning 116026 Peoples R China

ISBN: (纸本)142440164X

The proper selection and determination of partners is the key to the establishment of virtual logistic enterprises. With the rapid development of information technology, and the globalization and digitalization of the economy, the virtual logistic enterprises partners selection model based on CLIQUE is proposed to realize fast data mining in the bulkiness and high dimension information database of member enterprises provided by VLE information platform in order to select the candidate partners in the whole globe, or knowledge discover from indexes evaluation of partners. A simulating case will demonstrate the effectiveness of the selection method of the grid-based clustering algorithm.

关键词： virtual logistics enterprise (VLE) partner selection. grid cluster algorithm

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Evaluation of clustering algorithms for gene expression data

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BMC BIOINFORMATICS 2006年第s4期7卷 S17-S17页

作者： Datta, Susmita Datta, Somnath Univ Louisville Dept Bioinformat & Biostat Louisville KY 40202 USA

Background: cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. Results: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated. Conclusion: No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms.

关键词： cluster algorithm Validation Measure Statistical cluster Good Performer Similar Biological Function

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STEM: a tool for the analysis of short time series gene expression data

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BMC BIOINFORMATICS 2006年第1期7卷 191-191页

作者： Ernst, J Bar-Joseph, Z Carnegie Mellon Univ Sch Comp Sci Ctr Automated & Learning & Discovery Pittsburgh PA 15213 USA

Background: Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3-8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results: We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion: The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://***/similar to jernst/stem.

关键词： Gene Ontology cluster algorithm Model Profile Short Time Series Time Series Experiment

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Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

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BMC BIOINFORMATICS 2006年第1期7卷 397-397页

作者： Datta, Susmita Datta, Somnath Univ Louisville Dept Bioinformat & Biostat Louisville KY 40202 USA

Background: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. Results: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to

关键词： Gene Ontology cluster algorithm Functional Classis Statistical cluster Dissimilarity Measure

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Metric for measuring the effectiveness of clustering of DNA microarray expression

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BMC BIOINFORMATICS 2006年第Sup2期7卷 S5-S5页

作者： Loganantharaj, Raja Cheepala, Satish Clifford, John Univ Louisiana Bioinformat Res Lab Lafayette LA 70504 USA LSU Hlth Sci Ctr Dept Biochem & Mol Biol Shreveport LA 71130 USA Feist Weiller Canc Ctr Shreveport LA 71130 USA

Background: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances;hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. Results: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids. We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. Conclusion: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separati

关键词： Gene Ontology cluster algorithm Molecular Function Behavioral Group Saccharomyces Genome Database

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CoXpress: differential co-expression in gene expression data

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BMC BIOINFORMATICS 2006年第1期7卷 509-509页

作者： Watson, Michael Inst Anim Hlth Informat Grp Newbury RG20 7NN Berks England

Background: Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed. Results: We have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes. Conclusion: coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.

关键词： cluster algorithm Hierarchical cluster Analysis Acute Myeloid Leukaemia Acute Lymphoblastic Leukaemia Microarray Dataset

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