Clustering Of Gene Expression Data Research Articles

Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps

The importance of gene expression data in cancer diagnosis and treatment has become widely known by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality because of the overwhelming number of variables measured (genes) versus the small number of samples. Here, we use a two-step method to reduce the dimension of gene expression data and aim to address the problem of high dimensionality. First, we extract a subset of genes based on statistical characteristics of their corresponding gene expression levels. Then, for further dimensionality reduction, we apply diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set, in order to obtain efficient representation of data geometric descriptions. Finally, a neural network clustering theory, fuzzy ART, is applied to the resulting data to generate clusters of cancer samples. Experimental results on the small round blue-cell tumor data set, compared with other widely used clustering algorithms, such as the hierarchical clustering algorithm and K-means, show that our proposed method can effectively identify different cancer types and generate high-quality cancer sample clusters. The proposed feature selection methods and diffusion maps can achieve useful information from the multidimensional gene expression data and prove effective at addressing the problem of high dimensionality inherent in gene expression data analysis.

Efficiently Mining Time-Delayed Gene Expression Patterns

Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene x sample x time data sets to identify 3-D td-clusters.

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

Phasing of muscle gene expression with fasting-induced recovery growth in Atlantic salmon.

BackgroundMany fish species experience long periods of fasting in nature often associated with seasonal reductions in water temperature and prey availability or spawning migrations. During periods of nutrient restriction, changes in metabolism occur to provide cellular energy via catabolic processes. Muscle is particularly affected by prolonged fasting as myofibrillar proteins act as a major energy source. To investigate the mechanisms of metabolic reorganisation with fasting and refeeding in a saltwater stage of Atlantic salmon (Salmo salar L.) we analysed the expression of genes involved in myogenesis, growth signalling, lipid biosynthesis and myofibrillar protein degradation and synthesis pathways using qPCR.ResultsHierarchical clustering of gene expression data revealed three clusters. The first cluster comprised genes involved in lipid metabolism and triacylglycerol synthesis (ALDOB, DGAT1 and LPL) which had peak expression 3-14d after refeeding. The second cluster comprised ADIPOQ, MLC2, IGF-I and TALDO1, with peak expression 14-32d after refeeding. Cluster III contained genes strongly down regulated as an initial response to feeding and included the ubiquitin ligases MuRF1 and MAFbx, myogenic regulatory factors and some metabolic genes.ConclusionEarly responses to refeeding in fasted salmon included the synthesis of triacylglycerols and activation of the adipogenic differentiation program. Inhibition of MuRF1 and MAFbx respectively may result in decreased degradation and concomitant increased production of myofibrillar proteins. Both of these processes preceded any increase in expression of myogenic regulatory factors and IGF-I. These responses could be a necessary strategy for an animal adapted to long periods of food deprivation whereby energy reserves are replenished prior to the resumption of myogenesis.

Extracting biologically significant patterns from short time series gene expression data

BackgroundTime series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.ResultsWe developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data.ConclusionOur approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at .

An Iterative Data Mining Approach for Mining Overlapping Coexpression Patterns in Noisy Gene Expression Data

Clustering is concerned with the discovery of groupings of records in a database. Many clustering problems are defined as partitioning problems in the sense that the similar records are grouped into nonoverlapping partitions. However, the clustering of gene expression data to discover coexpressed genes may not always be meaningful if this problem is reduced into a partitioning problem. Due to the complexity of the underlying biological processes, a protein can interact with one or more other proteins belonging to different functional classes in order to perform a particular biological role. For this reason, when responding to different external stimulants, a gene that produces a particular protein can coexpress with more than one group of other genes. The gene can therefore belong to more than one group of coexpressed genes. This poses a challenge to many clustering algorithms as they are not originally developed to discover overlapping clusters in noisy gene expression data. In this paper, we propose an iterative data mining approach that consists of two phases as follows. In phase 1, a clustering algorithm is used to discover the initial, nonoverlapping partitioning of gene expression profiles in gene expression data. Then, the partition memberships of genes are redetermined iteratively in phase 2 by a pattern discovery technique so as to determine that if a gene should remain in the same partition, be moved to another partition, or be also grouped together with other genes in another partitions. The proposed approach has been tested with both artificial and real datasets. Experimental results show that it can improve the performances of existing clustering algorithms and is able to effectively discover overlapping clusters in noisy gene expression data.

VisHiC--hierarchical functional enrichment analysis of microarray data

Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic.

Exploring ant-based algorithms for gene expression data analysis

Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets

Motivation: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30 000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44 460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1).Availability: The program is freely available and can be downloaded from http://www.amdcc.org/bioinformatics/bioinformatics.aspx.Contact: rmcindoe@mail.mcg.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A Novel Approach for Discovering Overlapping Clusters in Gene Expression Data

Many existing clustering algorithms have been used to identify coexpressed genes in gene expression data. These algorithms are used mainly to partition data in the sense that each gene is allowed to belong only to one cluster. Since proteins typically interact with different groups of proteins in order to serve different biological roles, the genes that produce these proteins are therefore expected to coexpress with more than one group of genes. In other words, some genes are expected to belong to more than one cluster. This poses a challenge to gene expression data clustering as there is a need for overlapping clusters to be discovered in a noisy environment. For this task, we propose an effective information theoretical approach, which consists of an initial clustering phase and a second reclustering phase, in this paper. The proposed approach has been tested with both simulated and real expression data. Experimental results show that it can improve the performances of existing clustering algorithms and is able to effectively uncover interesting patterns in noisy gene expression data so that, based on these patterns, overlapping clusters can be discovered.

NIFTI: An evolutionary approach for finding number of clusters in microarray data

BackgroundClustering techniques are routinely used in gene expression data analysis to organize the massive data. Clustering techniques arrange a large number of genes or assays into a few clusters while maximizing the intra-cluster similarity and inter-cluster separation. While clustering of genes facilitates learning the functions of un-characterized genes using their association with known genes, clustering of assays reveals the disease stages and subtypes. Many clustering algorithms require the user to specify the number of clusters a priori. A wrong specification of number of clusters generally leads to either failure to detect novel clusters (disease subtypes) or unnecessary splitting of natural clusters.ResultsWe have developed a novel method to find the number of clusters in gene expression data. Our procedure evaluates different partitions (each with different number of clusters) from the clustering algorithm and finds the partition that best describes the data. In contrast to the existing methods that evaluate the partitions independently, our procedure considers the dynamic rearrangement of cluster members when a new cluster is added. Partition quality is measured based on a new index called Net InFormation Transfer Index (NIFTI) that measures the information change when an additional cluster is introduced. Information content of a partition increases when clusters do not intersect and decreases if they are not clearly separated. A partition with the highest Total Information Content (TIC) is selected as the optimal one. We illustrate our method using four publicly available microarray datasets.ConclusionIn all four case studies, the proposed method correctly identified the number of clusters and performs better than other well known methods. Our method also showed invariance to the clustering techniques.

Non-negative matrix factorization of gene expression profiles: a plug-in for BRB-ArrayTools

Non-negative matrix factorization (NMF) is an increasingly used algorithm for the analysis of complex high-dimensional data. BRB-ArrayTools is a widely used software system for the analysis of gene expression data with almost 9000 registered users in over 65 countries. We have developed a NMF analysis plug-in in BRB-ArrayTools for unsupervised sample clustering of microarray gene expression data. Our analysis tool also incorporates an algorithm for Semi-NMF which can handle both positive and negative elements for log-ratio data. Output includes a heat map of sample clusters and differentially expressed genes with extensive biological annotation. For comparison, output also includes the results of K-means clustering. The NMF analysis plug-in is freely available in BRB-ArrayTools for non-commercial users. BRB-ArrayTools can be downloaded at http://linus.nci.nih.gov/BRB-ArrayTools.html. The algorithms used for NMF and Semi-NMF are available at ftp://linus.nci.nih.gov/pub/NMF.

Incorporating heterogeneous biological data sources in clustering gene expression data

In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.

Clustering of gene expression data based on shape similarity.

A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which then are subject to a variational Bayes clustering plus a non-Bayesian (Silhouette) cluster statistic. The statistic shows an improved ability to identify the correct number of clusters and assign the components of cluster. Based on initial results for both generated test data and Escherichia coli microarray expression data and initial validation of the Escherichia coli results, it is shown that the method has promise in being able to better cluster time-series microarray data according to shape similarity.

Effective hybrid PSO and K-means clustering algorithm for gene expression data

DNA micro-array technology helps monitor the expression levels of thousands of genes. This paper presents clustering of gene expression data using particle swarm optimisation (PSO) and K-means algorithm combined. Recent studies have shown that partition-based clustering algorithms are more suitable for clustering large datasets. Partition-based K-means is used mostly because of its simple implementation and fast convergence. But it suffers local optima. PSO is a population-based stochastic search process, which searches automatically for the optimal solution in the search space. So, it is combined with K-means algorithm for clustering. In the previous PSO employing papers, particles flock at boundary. Our technique removes boundary blocks by moving the boundary particles towards the global best particle to improve effectiveness of PSO. The results of hybrid PSO, K-means and PSO algorithms are compared for several datasets. Among the three algorithms, the hybrid PSO algorithm performs well for most of the ...

Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics

The recent development of high-throughput molecular genetics technologies has brought a major impact to bioinformatics and systems biology. These technologies have made possible the measurement of the expression profiles of genes and proteins in a highly parallel and integrated fashion. The examination of the huge amounts of genomic and proteomic data holds the promise for understanding the complex interactions between genes and proteins, the functional processes of a cell, and the impact of various factors on a cell, and ultimately, for enabling the design of new technologies for intelligent management of diseases. This special issue focuses on modeling and processing of data arising in bioinformatics, genomics, and proteomics using signal processing methods. The importance of signal processing techniques is due to their important role in extracting, processing, and interpreting the information contained in genomic and proteomic data. It is our hope that signal processing methods will lead to new advances and insights in uncovering the structure, functioning and evolution of biological systems. The special issue consists of nine papers that span a wide range of problems and applications in bioinformatics, genomics, and proteomics such as design of compressive sensing microarrays, analysis of missing values in microarray data, and effect of imputation techniques on post genomic inference methods, RNA sequence alignment, detection of periodicity in genomic sequences and gene expression profiles, clustering and classification of gene and protein expression data, and intervention in probabilistic Boolean networks. Next, we will briefly introduce the papers reported in this special issue. W. Dai et al. analyze how to design a microarray that it is fit for compressive sensing and that captures also the biochemistry of probe-target DNA hybridization. Algorithms and design results are reported for determining probe sequences that satisfy the binding requirements and for evaluating the target concentrations. M. S. B. Sehgal et al. address the general problem of improving post genomic knowledge discovery procedures such as the selection of the most significant genes and inference of gene regulatory networks using missing microarray data imputation techniques. It is shown that instead of neglecting missing data, recycling microarray data via robust imputation techniques can yield substantial performance improvements in the subsequent post genomic discovery procedures. B.-J. Yoon developed a novel efficient and robust approach for fast and accurate structural alignment of RNAs, including pseudoknots. The proposed method turns out to accelerate the dynamic programming algorithm for family-specific models such as profile-csHMMs and CMs, and to be robust to small parameter changes that are present in the model used to predict the constraint. The paper by J. Epps explains in detail the origins of ambiguity in period estimation for symbolic sequences, and proposes a novel hybrid autocorrelation-IPDFT technique for periodicity characterization of sequences. W. Zhao et al. developed a novel algorithm for identification of genes involved in cyclic processes by combining gene expression analysis and prior knowledge. The proposed cyclic-genes detection algorithm is validated on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, and shown to represent a valuable technique for unveiling pathways related to cyclic processes. T. J. Hestilow and Y. Huang propose a novel method for gene clustering using the shape information of gene expression profiles. The shape information which is represented in terms of normalized and time-scaled forward first-order differences is then exploited by a variational Bayes clustering approach and a non-Bayesian (Silhouette) cluster statistic, and shown to yield promising results in clustering time-series microarray data. The paper by W. Zhao et al. proposes a new clustering approach to combine the traditional clustering methods with power spectral analysis of time series gene expression measurements. Simulation results confirm that the proposed clustering approach provides superior performance relative to hierarchical, K-means, and self-organizing maps, and yields additional information about temporal regulated genetic processes, for example, cell-cycle. T. T. Vu and U. M. Braga-Neto address the important problem of assessing the effectiveness of bagging in the classification of small-sample genomic and proteomic data sets. Representative experimental results are presented and discussed. Finally, the paper by B. Faryabi et al. studies the effects on intervention performance in the context of probabilistic Boolean networks due to a reduction in the values of the model parameters.

A niching genetic k-means algorithm and its applications to gene expression data

Partitional clustering is a common approach to cluster analysis. Although many algorithms have been proposed, partitional clustering remains a challenging problem with respect to the reliability and efficiency of recovering high quality solutions in terms of its criterion functions. In this paper, we propose a niching genetic k-means algorithm (NGKA) for partitional clustering, which aims at reliably and efficiently identifying high quality solutions in terms of the sum of squared errors criterion. Within the NGKA, we design a niching method, which encourages mating among similar clustering solutions while allowing for some competitions among dissimilar solutions, and integrate it into a genetic algorithm to prevent premature convergence during the evolutionary clustering search. Further, we incorporate one step of k-means operation into the regeneration steps of the resulted niching genetic algorithm to improve its computational efficiency. The proposed algorithm was applied to cluster both simulated data and gene expression data and compared with previous work. Experimental results clear show that the NGKA is an effective clustering algorithm and outperforms two other genetic algorithm based clustering methods implemented for comparison.

Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles

Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others. We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal et al. Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial dataset, and nine gene-expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data. The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.

A ground truth based comparative study on clustering of gene expression data

Given the variety of available clustering methods for gene expression data analysis, it is important to develop an appropriate and rigorous validation scheme to assess the performance and limitations of the most widely used clustering algorithms. In this paper, we present a ground truth based comparative study on the functionality, accuracy, and stability of five data clustering methods, namely hierarchical clustering, K-means clustering, self-organizing maps, standard finite normal mixture fitting, and a caBIG toolkit (VIsual Statistical Data Analyzer--VISDA), tested on sample clustering of seven published microarray gene expression datasets and one synthetic dataset. We examined the performance of these algorithms in both data-sufficient and data-insufficient cases using quantitative performance measures, including cluster number detection accuracy and mean and standard deviation of partition accuracy. The experimental results showed that VISDA, an interactive coarse-to-fine maximum likelihood fitting algorithm, is a solid performer on most of the datasets, while K-means clustering and self-organizing maps optimized by the mean squared compactness criterion generally produce more stable solutions than the other methods.

Analysis of a Gibbs sampler method for model-based clustering of gene expression data

Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software