Clustering Of Microarray Data Research Articles

Analysing microarray expression data through effective clustering

The recent advances in genomic technologies and the availability of large-scale microarray datasets call for the development of advanced data analysis techniques, such as data mining and statistical analysis to cite a few. Among the mining techniques proposed so far, cluster analysis has become a standard method for the analysis of microarray expression data. It can be used both for initial screening of patients and for extraction of disease molecular signatures. Moreover, clustering can be profitably exploited to characterize genes of unknown function and uncover patterns that can be interpreted as indications of the status of cellular processes. Finally, clustering biological data would be useful not only for exploring the data but also for discovering implicit links between the objects. To this end, several clustering approaches have been proposed in order to obtain a good trade-off between accuracy and efficiency of the clustering process. In particular, great attention has been devoted to hierarchical clustering algorithms for their accuracy in unsupervised identification and stratification of groups of similar genes or patients, while, partition based approaches are exploited when fast computations are required. Indeed, it is well known that no existing clustering algorithm completely satisfies both accuracy and efficiency requirements, thus a good clustering algorithm has to be evaluated with respect to some external criteria that are independent from the metric being used to compute clusters. In this paper, we propose a clustering algorithm called M-CLUBS (for Microarray data CLustering Using Binary Splitting) exhibiting higher accuracy than the hierarchical ones proposed so far while allowing a faster computation with respect to partition based approaches. Indeed, M-CLUBS is faster and more accurate than other algorithms, including k-means and its recently proposed refinements, as we will show in the experimental section. The algorithm consists of a divisive phase and an agglomerative phase; during these two phases, the samples are repartitioned using a least quadratic distance criterion possessing unique analytical properties that we exploit to achieve a very fast computation. M-CLUBS derives good clusters without requiring input from users, and it is robust and impervious to noise, while providing better speed and accuracy than methods, such as BIRCH, that are endowed with the same critical properties. Due to the structural feature of microarray data (they are represented as arrays of numeric values), M-CLUBS is suitable for analyzing them since it is designed to perform well for Euclidean distances. In order to stronger the obtained results we interpreted the obtained clusters by a domain expert and the evaluation by quality measures specifically tailored for biological validity assessment.

SAMSN1 Is Highly Expressed and Associated with a Poor Survival in Glioblastoma Multiforme

ObjectivesTo study the expression pattern and prognostic significance of SAMSN1 in glioma. MethodsAffymetrix and Arrystar gene microarray data in the setting of glioma was analyzed to preliminarily study the expression pattern of SAMSN1 in glioma tissues, and Hieratical clustering of gene microarray data was performed to filter out genes that have prognostic value in malignant glioma. Survival analysis by Kaplan-Meier estimates stratified by SAMSN1 expression was then made based on the data of more than 500 GBM cases provided by The Cancer Genome Atlas (TCGA) project. At last, we detected the expression of SAMSN1 in large numbers of glioma and normal brain tissue samples using Tissue Microarray (TMA). Survival analysis by Kaplan-Meier estimates in each grade of glioma was stratified by SAMSN1 expression. Multivariate survival analysis was made by Cox proportional hazards regression models in corresponding groups of glioma. ResultsWith the expression data of SAMSN1 and 68 other genes, high-grade glioma could be classified into two groups with clearly different prognoses. Gene and large sample tissue microarrays showed high expression of SAMSN1 in glioma particularly in GBM. Survival analysis based on the TCGA GBM data matrix and TMA multi-grade glioma dataset found that SAMSN1 expression was closely related to the prognosis of GBM, either PFS or OS (P<0.05). Multivariate survival analysis with Cox proportional hazards regression models confirmed that high expression of SAMSN1 was a strong risk factor for PFS and OS of GBM patients. ConclusionSAMSN1 is over-expressed in glioma as compared with that found in normal brains, especially in GBM. High expression of SAMSN1 is a significant risk factor for the progression free and overall survival of GBM.

Gene expression data clustering using a multiobjective symmetry based clustering technique

The invention of microarrays has rapidly changed the state of biological and biomedical research. Clustering algorithms play an important role in clustering microarray data sets where identifying groups of co-expressed genes are a very difficult task. Here we have posed the problem of clustering the microarray data as a multiobjective clustering problem. A new symmetry based fuzzy clustering technique is developed to solve this problem. The effectiveness of the proposed technique is demonstrated on five publicly available benchmark data sets. Results are compared with some widely used microarray clustering techniques. Statistical and biological significance tests have also been carried out.

Insights from the clustering of microarray data associated with the heart disease

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.

Comparative Proteomics of Thellungiella halophila Leaves from Plants Subjected to Salinity Reveals the Importance of Chloroplastic Starch and Soluble Sugars in Halophyte Salt Tolerance

Thellungiella halophila, a close relative of Arabidopsis, is a model halophyte used to study plant salt tolerance. The proteomic/physiological/transcriptomic analyses of Thellungiella plant leaves subjected to different salinity levels, reported herein, indicate an extraordinary ability of Thellungiella to adapt to large concentrations of exogenous saline by compartmentalizing Na(+) into cell vacuoles and accumulating proline and soluble sugars as organic osmolytes. Salinity stress stimulated the accumulation of starch in chloroplasts, which resulted in a greatly increased content of starch and total sugars in leaves. Comparative proteomics of Thellungiella leaves identified 209 salt-responsive proteins. Among these, the sequences of 108 proteins were strongly homologous to Arabidopsis protein sequences, and 30 had previously been identified as Thellungiella proteins. Functional classification of these proteins into 16 categories indicated that the majority are involved in carbohydrate metabolism, followed by those involved in energy production and conversion, and then those involved in the transport of inorganic ions. Pathway analysis revealed that most of the proteins are involved in starch and sucrose metabolism, carbon fixation, photosynthesis, and glycolysis. Of these processes, the most affected were starch and sucrose metabolism, which might be pivotal for salt tolerance. The gene expression patterns of the 209 salt-responsive proteins revealed through hierarchical clustering of microarray data and the expression patterns of 29 Thellungiella genes evaluated via quantitative RT-PCR were similar to those deduced via proteomic analysis, which underscored the possibility that starch and sucrose metabolism might play pivotal roles in determining the salt tolerance ability of Thellungiella. Our observations enabled us to propose a schematic representation of the systematic salt-tolerance phenotype in Thellungiella and suggested that the increased accumulation of starch, soluble sugars, and proline, as well as subcellular compartmentalization of sodium, might collectively denote important mechanisms for halophyte salt tolerance.

Genetic lineages of undifferentiated-type gastric carcinomas analysed by unsupervised clustering of genomic DNA microarray data

BackgroundIt is suspected that early gastric carcinoma (GC) is a dormant variant that rarely progresses to advanced GC. We demonstrated that the dormant and aggressive variants of tubular adenocarcinomas (TUBs) of the stomach are characterized by loss of MYC and gain of TP53 and gain of MYC and/or loss of TP53, respectively. The aim of this study is to determine whether this is also the case in undifferentiated-type GCs (UGCs) of different genetic lineages: one with a layered structure (LS+), derived from early signet ring cell carcinomas (SIGs), and the other, mostly poorly differentiated adenocarcinomas, without LS but with a minor tubular component (TC), dedifferentiated from TUBs (LS−/TC+).MethodsUsing 29 surgically resected stomachs with 9 intramucosal and 20 invasive UGCs (11 LS+ and 9 LS−/TC+), 63 genomic DNA samples of mucosal and invasive parts and corresponding reference DNAs were prepared from formalin-fixed, paraffin-embedded tissues with laser microdissection, and were subjected to array-based comparative genomic hybridization (aCGH), using 60K microarrays, and subsequent unsupervised, hierarchical clustering. Of 979 cancer-related genes assessed, we selected genes with mean copy numbers significantly different between the two major clusters.ResultsBased on similarity in genomic copy-number profile, the 63 samples were classified into two major clusters. Clusters A and B, which were rich in LS+ UGC and LS−/TC+ UGC, respectively, were discriminated on the basis of 40 genes. The aggressive pattern was more frequently detected in LS−/TC+ UGCs, (20/26; 77%), than in LS+UGCs (17/37; 46%; P = 0.0195), whereas no dormant pattern was detected in any of the UGC samples.ConclusionsIn contrast to TUBs, copy number alterations of MYC and TP53 exhibited an aggressive pattern in LS+ SIG at early and advanced stages, indicating that early LS+ UGCs inevitably progress to an advanced GC. Cluster B (enriched in LS−/TC+) exhibited more frequent gain of driver genes and a more frequent aggressive pattern than cluster A, suggesting potentially worse prognosis in UGCs of cluster B.

Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis

Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve satisfactory clustering results. Nevertheless, up to date, there are no comprehensive guidelines concerning how to choose proximity measures for clustering microarray data. Pearson is the most used proximity measure, whereas characteristics of other ones remain unexplored. In this paper, we investigate the choice of proximity measures for the clustering of microarray data by evaluating the performance of 16 proximity measures in 52 data sets from time course and cancer experiments. Our results support that measures rarely employed in the gene expression literature can provide better results than commonly employed ones, such as Pearson, Spearman, and euclidean distance. Given that different measures stood out for time course and cancer data evaluations, their choice should be specific to each scenario. To evaluate measures on time-course data, we preprocessed and compiled 17 data sets from the microarray literature in a benchmark along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.

A novel harmony search-K means hybrid algorithm for clustering gene expression data.

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.

Fuzzy C-means method with empirical mode decomposition for clustering microarray data

Microarray techniques have revolutionised genomic research by making it possible to monitor the expression of thousands of genes in parallel. The Fuzzy C-Means (FCM) method is an efficient clustering approach devised for microarray data analysis. However, microarray data contains noise, which would affect clustering results. In this paper, we propose to combine the FCM method with the Empirical Mode Decomposition (EMD) for clustering microarray data to reduce the effect of the noise. The results suggest the clustering structures of denoised microarray data are more reasonable and genes have tighter association with their clusters than those using FCM only.

Finding best algorithmic components for clustering microarray data

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.

Integrated Computational and Experimental Analysis of the Neuroendocrine Transcriptome in Genetic Hypertension Identifies Novel Control Points for the Cardiometabolic Syndrome

Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases such as the genomewide association study are powerful, yet a large gap exists between the fraction of population trait variance explained by such associations and total disease heritability. We developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes) to identify candidate genes for essential hypertension and the metabolic syndrome. We first undertook transcriptome profiling on adrenal glands from blood pressure extreme mouse strains: the hypertensive BPH (blood pressure high) and hypotensive BPL (blood pressure low). Microarray data clustering revealed a striking pattern of global underexpression of intermediary metabolism transcripts in BPH. The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes, and we then hypothesized that regulation through this motif contributed to the global underexpression. Luciferase reporter assays demonstrated transcriptional activity of the motif through transcription factors HOXA3, SRY, and YY1. We finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with blood pressure in a human population blood pressure extreme sample with the most extensive associations for YY1 tagging single nucleotide polymorphism rs11625658 on systolic blood pressure, diastolic blood pressure, body mass index, and fasting glucose. Meta-analysis extended the YY1 results into 2 additional large population samples with significant effects preserved on diastolic blood pressure, body mass index, and fasting glucose. The results outline an innovative, systematic approach to the genetic pathogenesis of complex cardiovascular disease traits and point to transcription factor YY1 as a potential candidate gene involved in essential hypertension and the cardiometabolic syndrome.

A prognostic gene expression signature in infratentorial ependymoma

Patients with ependymoma exhibit a wide range of clinical outcomes that are currently unexplained by clinical or histological factors. Little is known regarding molecular biomarkers that could predict clinical behavior. Since recent data suggest that these tumors display biological characteristics according to their location (cerebral vs. infratentorial vs. spinal cord), rather than explore a broad spectrum of ependymoma, we focused on molecular alterations in ependymomas arising in the infratentorial compartment. Unsupervised clustering of available gene expression microarray data revealed two major subgroups of infratentorial ependymoma. Group 1 tumors over expressed genes that were associated with mesenchyme, Group 2 tumors showed no distinct gene ontologies. To assess the prognostic significance of these gene expression subgroups, real-time reverse transcriptase polymerase chain reaction assays were performed on genes defining the subgroups in a training set. This resulted in a 10-gene prognostic signature. Multivariate analysis showed that the 10-gene signature was an independent predictor of recurrence-free survival after adjusting for clinical factors. Evaluation of an external dataset describing subgroups of infratentorial ependymomas showed concordance of subgroup definition, including validation of the mesenchymal subclass. Importantly, the 10-gene signature was validated as a predictor of recurrence-free survival in this dataset. Taken together, the results indicate a link between clinical outcome and biologically identified subsets of infratentorial ependymoma and offer the potential for prognostic testing to estimate clinical aggressiveness in these tumors.

Improved differential evolution for microarray analysis

Clustering is an important tool for analysing the microarray data to identify groups of co-expressed genes. The problem of fuzzy clustering in microarray data motivated us to develop an improved clustering algorithm. In this paper, an improved differential evolution based fuzzy clustering technique is proposed. The performance of the proposed improved differential evolution based fuzzy clustering technique has been compared with other state-of-the-art clustering algorithms for publicly available benchmark microarray data sets. Statistical and biological significance tests have been carried out to establish the statistical superiority of the proposed clustering approach and biological relevance of clusters of co-expressed genes, respectively.

Differentiation-Promoting Culture of Competent and Noncompetent Keratinocytes Identifies Biomarkers for Head and Neck Cancer

Aberrant contact-inhibited proliferation and differentiation induction couple with tumor severity, albeit with an imprecise association with prognosis. Assessment of contact inhibition and differentiation-promoting culture in this study of normal and immortalized oral keratinocytes (NOK and SVpgC2a, respectively) demonstrated elevated cloning ability and saturation density in the immortalized versus normal state, including consistent absence of differentiated morphological features. Transcriptomic analysis implicated 48 gene ontology categories, 8 molecular networks, and 10 key regulator genes in confluency-induced differentiation of NOK, all of which remained nonregulated in SVpgC2a. The SVpgC2a versus NOK transcriptome enriched 52 gene ontology categories altogether, 18 molecular networks, and 39 key regulator genes, several of which were associated with epithelial-mesenchymal transition. Assessment of the previously described gene sets relative to training data sets of head and neck squamous cell carcinoma samples, one including data on tumor differentiation and patient outcome and one present in the Human Gene Expression Map, identified four genes with association to poor survival (COX7A1, MFAP5, MPDU1, and POLD1). This gene set predicted poor outcome in an independent data set of 71 head and neck squamous cell carcinomas. The present study defines, for the first time to our knowledge, the broad gene spectrum that couples to induction, and loss, of oral keratinocyte differentiation. Bioinformatics assessments of the results relative to clinical data generated novel differentiation-related tumor biomarkers relevant to patient outcome.

Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

Comprehensive evaluation of matrix factorization methods for the analysis of DNA microarray gene expression data

BackgroundClustering-based methods on gene-expression analysis have been shown to be useful in biomedical applications such as cancer subtype discovery. Among them, Matrix factorization (MF) is advantageous for clustering gene expression patterns from DNA microarray experiments, as it efficiently reduces the dimension of gene expression data. Although several MF methods have been proposed for clustering gene expression patterns, a systematic evaluation has not been reported yet.ResultsHere we evaluated the clustering performance of orthogonal and non-orthogonal MFs by a total of nine measurements for performance in four gene expression datasets and one well-known dataset for clustering. Specifically, we employed a non-orthogonal MF algorithm, BSNMF (Bi-directional Sparse Non-negative Matrix Factorization), that applies bi-directional sparseness constraints superimposed on non-negative constraints, comprising a few dominantly co-expressed genes and samples together. Non-orthogonal MFs tended to show better clustering-quality and prediction-accuracy indices than orthogonal MFs as well as a traditional method, K-means. Moreover, BSNMF showed improved performance in these measurements. Non-orthogonal MFs including BSNMF showed also good performance in the functional enrichment test using Gene Ontology terms and biological pathways.ConclusionsIn conclusion, the clustering performance of orthogonal and non-orthogonal MFs was appropriately evaluated for clustering microarray data by comprehensive measurements. This study showed that non-orthogonal MFs have better performance than orthogonal MFs and K-means for clustering microarray data.

Improvement of new automatic differential fuzzy clustering using SVM classifier for microarray analysis

In recent year, the problem of clustering in microarray data has been gaining significant attention. However most of the clustering methods attempt to find the group of genes where the number of cluster is known a priori. This fact motivated us to develop a new real-coded improved differential evolution based automatic fuzzy clustering algorithm which automatically evolves the number of clusters as well as the proper partitioning of a gene expression data set. To improve the result further, the clustering method is integrated with a support vector machine, a well-known technique for supervised learning. A fraction of the gene expression data points selected from different clusters based on their proximity to the respective centers, is used for training the SVM. The clustering assignments of the remaining gene expression data points are thereafter determined using the trained classifier. The performance of the proposed clustering technique has been demonstrated on five gene expression data sets by comparing it with the differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering and well known Fuzzy C-Means algorithm. Statistical significance test has been carried out to establish the statistical superiority of the proposed clustering approach. Biological significance test has also been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of genes. The processed data sets and the matlab version of the software are available at http://bio.icm.edu.pl/∼darman/IDEAFC-SVM/.

Gene Expression Rhythms in the Mussel Mytilus galloprovincialis (Lam.) across an Annual Cycle

Seasonal environmental changes may affect the physiology of Mytilus galloprovincialis (Lam.), an intertidal filter-feeder bivalve occurring commonly in Mediterranean and Atlantic coastal areas. We investigated seasonal variations in relative transcript abundance of the digestive gland and the mantle (gonads) of males and females. To identify gene expression trends – in terms of relative mRNA abundance- we used a medium-density cDNA microarray (1.7 K probes) in dual-color competitive hybridization analyses. Hierarchical clustering of digestive gland microarray data showed two main branches, distinguishing profiles associated with the “hot” months (May–August) from the other months. Genes involved in chitin metabolism, associated with mussel nutrition and digestion showed higher mRNA levels during summer. Moreover, we found different gene transcriptomic patterns in the digestive glands of males when compared to females, during the four stages of mussel gonadal development. Microarray data from gonadal transcripts also displayed clear patterns during the different developmental phases respect to the resting period (stage I) with peak relative mRNA abundance at the ripe phase (stage III) for both sexes. These data showed a clear temporal pattern in transcriptomic profiles of mussels sampled over an annual cycle. Physiological response to thermal variation, food availability, and reproductive status across months may contribute to variation in relative mRNA abundance.

Gene Expression in the Epididymis of Normal and Vasectomized Men: What Can We Learn About Human Sperm Maturation?

Anatomically, the human epididymis is unusual when compared with the excurrent duct of other eutherian mammals. Furthermore, clinical observations suggest that it may not be as important for sperm maturation as is the case for laboratory animals. In contrast, hierarchical clustering of microarray data of epididymides from normal men revealed 2274 modulated qualifiers between the epididymal segments, 1184, 713, and 269 of them being highly expressed in the caput, corpus, and cauda, respectively. The organization of qualifiers according to their similarities by gene ontology indicated that caput transcripts are dedicated to cell-cell adhesion, whereas the corpus is characterized by genes involved in response to other organisms (ie, defense mechanisms) and the cauda transcriptome is specialized in muscle contraction and establishment of localization. A region-specific gene expression pattern thus characterizes the human epididymis as in animal models. In humans, vasectomies have consequences on the epididymal transcriptome. Cluster analysis revealed that 1363 genes are expressed in both normal and vasectomized epididymides, whereas 911 and 660 of them are specifically expressed in normal and vasectomized epididymides, respectively. Three of the affected genes are particularly interesting because of their involvement in sperm biochemical remodeling during epididymal transit: dicarbonyl/l-xylulose reductase, Niemann-Pick disease, type C2, and cysteine-rich secretory protein 1. In some vasovasostomized men, these modifications in gene expression induced by vasectomy are irreversible, thus affecting the biochemical parameters, and potentially, the function of their ejaculated sperm. This may explain the discrepancies between a surgically successful vasovasostomy and fertility recovery.

A novel approach to the clustering of microarray data via nonparametric density estimation

BackgroundCluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.ResultsHere, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.ConclusionsThe proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.