Clustering Of Microarray Data Research Articles

An Up-Down Bit Pattern Approach to Coregulated and Negative-Coregulated Gene Clustering of Microarray Data

Biclustering, which performs simultaneous clustering of rows (e.g., genes) and columns (e.g., conditions), has been shown to be important for analyzing microarray data. To find biclusters, there have been many methods proposed. Most of these methods can find only clusters with coregulated patterns, which means that the expression levels of genes in a found cluster rise and fall simultaneously. However, for real microarray data, there exist negative-correlated patterns, which means that the tendencies of expression levels of some genes may be completely inverse to those of the other genes under some conditions. Although one method called Co-gclustering was proposed to simultaneously find clusters with correlated and negative-correlated patterns, its time complexity is exponential to the number of conditions, which may not be efficient. Therefore, in this article, we propose a new method, Up-Down Bit pattern (UDB), to efficiently find clusters with correlated and negative-correlated patterns. First, we utilize up-down bit patterns to record those condition pairs where one gene is upregulated or downregulated. One gene is upregulated (or downregulated) under condition pair a and b if its expression level shows an upward (or downward) tendency from condition a to condition b. Then, we apply a heuristic idea on these up-down bit patterns to efficiently find clusters, which will reduce the time complexity from exponential time to polynomial time. From the experimental results, we show that the UDB method is more efficient than the Co-gclustering method.

Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as a multiobjective optimization is used, which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. In this regards, a new multiobjective differential evolution based fuzzy clustering technique has been proposed. Subsequently, for supervised learning, a fuzzy majority voting scheme along with support vector machine is used to integrate the clustering information from all the solutions in the resultant Pareto-optimal set. The performances of the proposed clustering techniques have been demonstrated on five publicly available benchmark microarray data sets. A detail comparison has been carried out with multiobjective genetic algorithm based fuzzy clustering, multiobjective differential evolution based fuzzy clustering, single objective versions of differential evolution and genetic algorithm based fuzzy clustering as well as well known fuzzy c-means algorithm. While using support vector machine, comparative studies of the use of four different kernel functions are also reported. Statistical significance test has been done to establish the statistical superiority of the proposed multiobjective clustering approach. Finally, biological significance test has been carried out using a web based gene annotation tool to show that the proposed integrated technique is able to produce biologically relevant clusters of coexpressed genes.

Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as a multiobjective ...

Gene expression data analysis using multiobjective clustering improved with SVM based ensemble.

Microarray technology facilitates the monitoring of the expression levels of thousands of genes over different experimental conditions simultaneously. Clustering is a popular data mining tool which can be applied to microarray gene expression data to identify co-expressed genes. Most of the traditional clustering methods optimize a single clustering goodness criterion and thus may not be capable of performing well on all kinds of datasets. Motivated by this, in this article, a multiobjective clustering technique that optimizes cluster compactness and separation simultaneously, has been improved through a novel support vector machine classification based cluster ensemble method. The superiority of MOCSVMEN (MultiObjective Clustering with Support Vector Machine based ENsemble) has been established by comparing its performance with that of several well known existing microarray data clustering algorithms. Two real-life benchmark gene expression datasets have been used for testing the comparative performances of different algorithms. A recently developed metric, called Biological Homogeneity Index (BHI), which computes the clustering goodness with respect to functional annotation, has been used for the comparison purpose.

Collagen induced arthritis (CIA) in mice features regulatory transcriptional network connecting major histocompatibility complex (MHC H2) with autoantigen genes in the thymus

Considering that imbalance of central tolerance in the thymus contributes to aggressive autoimmunity, we compared the expression of peripheral tissue autoantigens (PTA) genes, which are involved in self-representation in the thymic stroma, of two mouse strains; DBA-1/J (MHC- H2 q ) susceptible and DBA-2/J (MHC- H2 d ) resistant to collagen induced arthritis (CIA). We evaluate whether these strains differ in their thymic gene expression, allowing identification of genes that might play a role in susceptibility/resistance to CIA. Microarray profiling showed that 1093 PTA genes were differentially modulated between collagen immunized DBA-1/J and DBA-2/J mice. These genes were assigned to 17 different tissues/organs, including joints/bone, characterizing the promiscuous gene expression (PGE), which is implicated in self-representation. Hierarchical clustering of microarray data and quantitative RT-PCR analysis showed that Aire (autoimmune regulator), an important regulator of the PGE process, Aire-dependent (insulin), Aire-independent ( Col2A1 and Gad67), and other 22 joint/bone autoantigen genes were down-regulated in DBA-1/J compared with DBA-2/J in the thymus. Considering the importance of MHC- H2 in peptide-self presentation and autoimmunity susceptibility, we reconstructed transcriptional networks of both strains based on actual microarray data. The networks clearly demonstrated different MHC-H2 transcriptional interactions with PTAs genes. DBA-1/J strain featured MHC- H2 as a node influencing downstream genes. Differently, in DBA-2/J strain network MHC- H2 was exclusively self-regulated and does not control other genes. These findings provide evidence that CIA susceptibility in mice may be a reflex of a cascade-like transcriptional control connecting different genes to MHC- H2 in the thymus.

Including transcription factor information in the superparamagnetic clustering of microarray data

In this work, we modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. With this modified algorithm which we call SPCTF, we analyze the Spellman et al. microarray data for cell cycle genes in yeast, and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worthy of further experimental studies because they would probably lead to new cell cycle genes. Finally, our idea of introducing the available information about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms.

Identification of IgE sequential epitopes of lentil (Len c 1) by means of peptide microarray immunoassay

Lentils are often responsible for allergic reactions to legumes in Mediterranean children. Although the primary sequence of the major allergen Len c 1 is known, the location of the IgE-binding epitopes remains undefined. We sought to identify IgE-binding epitopes of Len c 1 and relate epitope binding to clinical characteristics. One hundred thirty-five peptides corresponding to the primary sequence of Len c 1 were probed with sera from 33 patients with lentil allergy and 15 nonatopic control subjects by means of microarray immunoassay. Lentil-specific IgE levels, skin prick test responses, and clinical reactions to lentil were determined. Epitopes were defined as overlapping signal above interslide and intraslide cutoffs and confirmed by using inhibition assays with a peptide from the respective region. Hierarchic clustering of microarray data was used to correlate binding patterns with clinical findings. The patients with lentil allergy specifically recognized IgE-binding epitopes located in the C-terminal region between peptides 107 and 135. Inhibition experiments confirmed the specificity of IgE binding in this region, identifying different epitopes. Linkage of cluster results with clinical data and lentil-specific IgE levels displayed a positive correlation between lentil-specific IgE levels, epitope recognition, and respiratory symptoms. Modeling based on the 3-dimensional structure of a homologous soy vicilin suggests that the Len c 1 epitopes identified are exposed on the surface of the molecule. Several IgE-binding sequential epitopes of Len c 1 have been identified. Epitopes are located in the C-terminal region and are predicted to be exposed on the surface of the protein. Epitope diversity is positively correlated with IgE levels, pointing to a more polyclonal IgE response.

Gene microarray data analysis using parallel point-symmetry-based clustering.

Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or non-convex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetry-based K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.

Spectral Clustering of Microarray Data Elucidates the Roles of Microenvironment Remodeling and Immune Responses in Survival of Head and Neck Squamous Cell Carcinoma

To identify functionally related prognostic gene sets for head and neck squamous cell carcinoma (HNSCC) by unsupervised statistical analysis of microarray data. Microarray analysis was performed on 14 normal oral epithelium and 71 HNSCCs from patients with outcome data. Spectral clustering (SC) analysis of the data set identified multiple vectors representing distinct aspects of gene expression heterogeneity between samples. Gene ontology (GO) analysis of vector gene lists identified gene sets significantly enriched within defined biologic pathways. The prognostic significance of these was established by Cox survival analysis. The most influential SC vectors were V2 and V3. V2 separated normal from tumor samples. GO analysis of V2 gene lists identified pathways with heterogeneous expression between HNSCCs, notably focal adhesion (FA)/extracellular matrix remodeling and cytokine-cytokine receptor (CR) interactions. Similar analysis of V3 gene lists identified further heterogeneity in CR pathways. V2CR genes represent an innate immune response, whereas high expression of V3CR genes represented an adaptive immune response that was not dependent on human papillomavirus status. Survival analysis demonstrated that the FA gene set was prognostic of poor outcome, whereas classification for adaptive immune response by the CR gene set was prognostic of good outcome. A combined FA&CR model dramatically exceeded the performance of current clinical classifiers (P < .001 in our cohort and, importantly, P = .007 in an independent cohort of 60 HNSCCs). The application of SC and GO algorithms to HNSCC microarray data identified gene sets highly significant for predicting patient outcome. Further large-scale studies will establish the usefulness of these gene sets in the clinical management of HNSCC.

LCE: a link-based cluster ensemble method for improved gene expression data analysis

It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically combining multiple data partitions from different clusterings to improve both the robustness and quality of the clustering result. However, many existing ensemble techniques use an association matrix to summarize sample-cluster co-occurrence statistics, and relations within an ensemble are encapsulated only at coarse level, while those existing among clusters are completely neglected. Discovering these missing associations may greatly extend the capability of the ensemble methodology for microarray data clustering. The link-based cluster ensemble (LCE) method, presented here, implements these ideas and demonstrates outstanding performance. Experiment results on real gene expression and synthetic datasets indicate that LCE: (i) usually outperforms the existing cluster ensemble algorithms in individual tests and, overall, is clearly class-leading; (ii) generates excellent, robust performance across different types of data, especially with the presence of noise and imbalanced data clusters; (iii) provides a high-level data matrix that is applicable to many numerical clustering techniques; and (iv) is computationally efficient for large datasets and gene clustering. Online supplementary and implementation are available at: http://users.aber.ac.uk/nii07/bioinformatics2010. Supplementary data are available at Bioinformatics online.

A temporal precedence based clustering method for gene expression microarray data.

BackgroundTime-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.ResultsA gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system.ConclusionsOur experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits.

Data-Fusion in Clustering Microarray Data: Balancing Discovery and Interpretability

While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.

High Temperatures Cause Male Sterility in Rice Plants with Transcriptional Alterations During Pollen Development

Plant male reproductive development is highly organized and sensitive to various environmental stressors, including high temperature. We have established an experimental procedure to evaluate high temperature injury in japonica rice plants. High temperature treatment (39 degrees C/30 degrees C) starting at the microspore stage repeatedly reduced spikelet fertility in our system. Morphological observations revealed that pollen viability in plants exposed to high temperatures was lower than that in control plants. Most pollen grains in high temperature-treated plants displayed a normal round shape and stained reddish purple with Alexander's reagent; however, the pollen grains were very poorly attached and displayed limited germination on the stigma. To investigate gene regulatory mechanisms in the anther in high temperature environments, DNA microarray analysis was performed by comparing non-treated samples with samples treated with 2-4 d of high heat. Genes responsive to high temperatures were identified from clustering of microarray data. Among these, at least 13 were designated as high temperature-repressed genes in the anther. Expression analyses revealed that these genes were expressed specifically in the immature anther mainly in the tapetum at the microspore stage and down-regulated after 1 d of high temperature. The expression levels of Osc6, OsRAFTIN and TDR, which are tapetum-specific genes, were unaffected by high temperatures. These results suggest that not all tapetal genes are inhibited by increased temperatures and the tapetum itself is not degraded in such an environment. However, high temperatures may disrupt some of the tapetum functions required for pollen adhesion and germination on the stigma.

A simple and robust algorithm for microarray data clustering based on gene population‐variance ratio metric

With the advent of the microarray technology, the field of life science has been greatly revolutionized, since this technique allows the simultaneous monitoring of the expression levels of thousands of genes in a particular organism. However, the statistical analysis of expression data has its own challenges, primarily because of the huge amount of data that is to be dealt with, and also because of the presence of noise, which is almost an inherent characteristic of microarray data. Clustering is one tool used to mine meaningful patterns from microarray data. In this paper, we present a novel method of clustering yeast microarray data, which is robust and yet simple to implement. It identifies the best clusters from a given dataset on the basis of the population of the clusters as well as the variance of the feature values of the members from the cluster-center. It has been found to yield satisfactory results even in the presence of noisy data.

GOAPhAR: An Integrative Discovery Tool for Annotation, Pathway Analysis

We have developed the web based tool GOAPhAR (Gene Ontology, Annotations and Pathways for Array Research), that integrates information from disparate sources regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, Gene Ontology, publicly available microarray datasets and tools for statistically validating clusters in microarray data. Genes of interest can be input as Affymetrix probe identifiers, Genbank, or Unigene identifiers for human, mouse or rat genomes. Results are provided in a user friendly interface with hyperlinks to the sources of information.

Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

BackgroundInterpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.ResultsIn this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased.ConclusionIt was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.

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.

Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray Data

This paper presents Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) of microarray data for finding differentially expressed genes (DEGs) from two-sample microarray experiments. The concept of fuzzy membership is introduced to transform the hard adaptive subspace iteration (ASI) algorithm into a fuzzy-ASI algorithm to perform two-way clustering. The proposed approach follows a progressive framework to assign a relevance value to genes associated with each cluster. Subsequently, each gene cluster is scored and ranked based on its potential to provide a correct classification of the sample classes. These ranks are converted into P values using the R-test, and the significance of each gene is determined. A fivefold validation is performed on the DEGs selected using the proposed approach. Empirical analyses on a number of simulated microarray data sets are conducted to quantify the results obtained using the proposed approach. To exemplify the efficacy of the proposed approach, further analyses on different real microarray data sets are also performed.

Yin Yang 1 Modulates Taxane Response in Epithelial Ovarian Cancer

Survival of ovarian cancer patients is largely dictated by their response to chemotherapy, which depends on underlying molecular features of the malignancy. We previously identified YIN YANG 1 (YY1) as a gene whose expression is positively correlated with ovarian cancer survival. Herein, we investigated the mechanistic basis of this association. Epigenetic and genetic characteristics of YY1 in serous epithelial ovarian cancer were analyzed along with YY1 mRNA and protein. Patterns of gene expression in primary serous epithelial ovarian cancer and in the NCI60 database were investigated using computational methods. YY1 function and modulation of chemotherapeutic response in vitro was studied using small interfering RNA knockdown. Microarray analysis showed strong positive correlation between expression of YY1 and genes with YY1 and transcription factor E2F binding motifs in ovarian cancer and in the NCI60 cancer cell lines. Clustering of microarray data for these genes revealed that high YY1/E2F3 activity positively correlates with survival of patients treated with the microtubule-stabilizing drug paclitaxel. Increased sensitivity to taxanes, but not to DNA cross-linking platinum agents, was also characteristic of NCI60 cancer cell lines with a high YY1/E2F signature. YY1 knockdown in ovarian cancer cell lines results in inhibition of anchorage-independent growth, motility, and proliferation but also increases resistance to taxanes, with no effect on cisplatin sensitivity. These results, together with the prior demonstration of augmentation of microtubule-related genes by E2F3, suggest that enhanced taxane sensitivity in tumors with high YY1/E2F activity may be mediated by modulation of putative target genes with microtubule function.

Papers on normalization, variable selection, classification or clustering of microarray data

Over the last decade or so, there have been large numbers of methods published on approaches for normalization, variable (gene) selection, classification, and clustering of microarray data. As indicated in the scope document for Bioinformatics, this requires papers describing new methods for these problems to meet a very high standard, showing important improvement in results for real biological data, as well as novelty. In this editorial, we describe some standards that need to be met for papers in these areas to be seriously considered. We ask that prospective authors consider these points carefully before submission of their papers to Bioinformatics. The Role of Simulation. Simulation can be useful in investigating the properties of various methods of data analysis. Yet there are important barriers to credible use of simulation in microarray studies, largely due to what we don’t know about the statistical distribution of measured gene expression levels. First, the distribution across transcripts of true expression values is dependent on the biological state of the tissue or cell, and for a given state this is unknown, even in distributional form, and may further exhibit genespecific and platform-specific effects. Second, the correlation within biological replicates of true expression is unknown, and is likely unknowable in detail given that it is