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Evaluation of clustering algorithms for gene expression data

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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Real-time electron clustering in an event-driven hybrid pixel detector

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ULTRAMICROSCOPY 2024年 255卷 113864页

作者： Kuttruff, J. Holder, J. Meng, Y. Baum, P. Univ Konstanz Fachbereich Phys D-78464 Constance Germany

Event-driven hybrid pixel detectors with nanosecond time resolution have opened up novel pathways in modern ultrafast electron microscopy, for example in hyperspectral electron-energy loss spectroscopy or free-electron quantum optics. However, the impinging electrons typically excite more than one pixel of the device, and an efficient algorithm is therefore needed to convert the measured pixel hits to real single-electron events. Here we present a robust clustering algorithm that is fast enough to find clusters in a continuous stream of raw data in real time. Each tuple of position and arrival time from the detector is continuously compared to a buffer of previous hits until the probability of a merger with an old event becomes irrelevant. In this way, the computation time becomes independent of the density of electron arrival and the algorithm does not break the operation chain. We showcase the performance of the algorithm with a 'timepix' camera in two regimes of electron microscopy, in continuous beam emission and laser-triggered femtosecond mode.

关键词： Direct electron detector cluster algorithm Femtosecond electron pulses Poisson statistics

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Detection of mutual phase synchronization in multivariate signals and application to phase ensembles and chaotic data

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Physical Review E 2003年第3期68卷 036219-036219页

作者： A. Hutt A. Daffertshofer U. Steinmetz []Weierstrass-Institute for Applied Analysis and Stochastics Mohrenstrasse 39 10117 Berlin Germany

This work presents a method for the detection of mutual phase synchronization in nonstationary time series. We show how the application of a cluster algorithm that considers spatiotemporal structures of data follows from the general condition of phase-synchronized data. In view of the topology of phasic data, we reformulate the K-means cluster algorithm on a flat torus and apply a segmentation index derived in an earlier work [A. Hutt and H. Riedel, Physica D 177, 203 (2003)]. This index is extended by means of averaging in order to reflect phase synchronization in ensembles of multivariate time series. The method is illustrated using simulated multivariate phase dynamics and arrays of chaotic systems, in which temporal segments of phase-synchronized states are registered. A comparison with results from an existing bivariate synchronization index reveals major advantages of our method.

关键词： .spatiotemporal reveals bivariate torus cluster algorithm chaotic systems phasic phase synchronization synchronization index multivariate signals chaotic data segmentation index

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Incremental genetic K-means algorithm and its application in gene expression data analysis

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BMC BIOINFORMATICS 2004年第1期5卷 172-172页

作者： Lu, Y Lu, SY Fotouhi, F Deng, YP Brown, SJ Univ So Mississippi Dept Biol Sci Hattiesburg MS 39406 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kansas State Univ Div Biol Manhattan KS 66506 USA

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://***/proj/FGKA/***. Conclusions: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

关键词： cluster algorithm Functional Category Mutation Operator Mutation Probability Gene Expression Data Analysis

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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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clustering proteins from interaction networks for the prediction of cellular functions -: art. no. 95

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BMC BIOINFORMATICS 2004年第1期5卷 95-95页

作者： Brun, C Herrmann, C Guénoche, A Univ Mediterranee INSERM CNRS IBDMLab Genet & Physiol Dev F-13288 Marseille 9 France CNRS Inst Math Luminy F-13288 Marseille 9 France

Background: Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes. Results: Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins. Conclusion: We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.

关键词： cluster algorithm Interaction Network Interaction Partner Interaction Graph Hybrid Procedure

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A practical comparison of two K-Means clustering algorithms

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BMC BIOINFORMATICS 2008年第s6期9卷 S19-S19页

作者： Wilkin, Gregory A. Huang, Xiuzhen Arkansas State Univ Dept Comp Sci State Univ AR 72467 USA

Background: Data clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementations of clustering algorithms yield the same performance or the same clusters. Results: In this paper, we study two implementations of a general method for data clustering: k-means clustering. Our experimentation compares the running times and distance efficiency of Lloyd's K-means clustering and the Progressive Greedy K-means clustering. Conclusion: Based on our implementation, not just in processing time, but also in terms of mean squared-difference (MSD), Lloyd's K-means clustering algorithm is more efficient. This analysis was performed using both a gene expression level sample and on randomly-generated datasets in three-dimensional space. However, other circumstances may dictate a different choice in some situations.

关键词： cluster algorithm cluster Center cluster Representative Close Center Proximity Relationship

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

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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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