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

Microarray data are a rich source of information, containing the collected expression values of thousands of genes for well defined states of a cell or tissue. Vast amounts of data (thousands of arrays) are publicly available and ready for analysis, e.g. to scrutinise correlations between genes at the level of gene expression. The large variety of arrays available makes it possible to combine different independent experiments to extract new knowledge. Starting with a large set of data, relevant information can be isolated for further analysis. To extract the required information from data sets of such size and complexity requires an appropriate and powerful analysis method. In this study, we chose to use an unsupervised hierarchical clustering algorithm, Chaotic Map Clustering (CMC), in a coupled two-way approach to analyse such data. However, the clustering approach is intrinsically difficult, both in terms of the unknown structure of the data and interpretation of the clustering results. It is therefore critical to evaluate the quality of any unsupervised procedure for such a complex set of data and to validate the clustering results, separating those clusters that are due simply to noise or statistical fluctuations. We used a resampling method to perform this validation. The resampling procedure applies the clustering algorithm to a large number of random sub-samples of the original data matrix and, consequently, the whole process becomes computationally intensive and time consuming. Using Grid technology, we show that we can drastically speed up this process by distributing the clustering of each matrix to a separate worker node, and thus retrieve resampling results within a few hours instead of several days. Further, we offer an online service to cluster large microarray data sets and conduct the subsequent validation described in this paper.

DNA microarray technology is one of the major recent advances in biotechnology [1]. In particular, their ability to measure the expression of thousands of genes simultaneously under a certain condition makes them suitable for several biological applications, such as the functional analysis of genes and identification of significantly over- or under-expressed genes in complex diseases like cancer. An application of great

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

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.

Point symmetry-based clustering is an important unsupervised learning tool for recognizing symmetrical convex or non-convex shaped clusters, even in the microarray datasets. To enable fast clustering of this large data, in this article, a distributed space and time-efficient scalable parallel approach for point symmetry-based K-means algorithm has been proposed. A natural basis for analyzing gene expression data using this symmetry-based algorithm, is to group together genes with similar symmetrical patterns of expression. This new parallel implementation satisfies the quadratic reduction in timing, as well as the space and communication overhead reduction without sacrificing the quality of clustering solution. The parallel point symmetry based K-means algorithm is compared with another newly implemented parallel symmetry-based K-means and existing parallel K-means over four artificial, real-life and benchmark microarray datasets, to demonstrate its superiority,both in timing and validity.

The use of microarray data for the feature (gene) selection is continuously increasing in the field of health care to diagnose the disease. Now it becomes a trend to find the subset of feature by using traditional algorithms. Most of the researches have used intelligent algorithm for the same to predict the diseases and take necessary action as per the requirement. In addition, a minimum feature set can be useful to prognosis the disease in contrast to a huge feature set. Inspired by this, we built a model based on genetic algorithm to select the minimum feature set with high accuracy from large microarray data. We have applied the machine learning classifier to get the accuracy of the features. For experimental analysis, we use the cancer based microarray gene expressed data and compare the simulation result with Differential Evolution.

A question has been raised in several publications as to whether or not the expression levels or their logarithms for different genes are normally distributed. To answer this question would require a large data set where both biological variability and technological noise are present. An earlier attempt to test this assumption was limited to technical replicates and did not take multiplicity of tests into account when assessing the net results of goodness-of-fit testing. Therefore, the problem calls for further exploration. We applied several statistical tests to a large set of high-density oligonucleotide microarray data in order to systematically test for log-normality of expression levels for all the reporter genes. The multiple testing aspect of the problem was addressed by designing a pertinent resampling procedure. The results of testing did not reject normality of log-intensities in the non-normalized data under study. However, the global log-normality hypothesis was rejected beyond all reasonable doubt when the data were normalized by the quantile normalization procedure. Our results are consistent with the hypothesis that non-normalized expression levels of different genes are approximately log-normally distributed. The quantile normalization causes dramatic changes in the shape of marginal distributions of log-intensities which may be an indication that this procedure interferes not only in the technological noise but the true biological signal as well. This possibility invites a special investigation.

In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies.

A genome-wide survey of Catharanthus roseus receptor-like kinase1-like kinases (CrRLK1Ls) in rice revealed that the pattern of expression by some CrRLK1Ls is controlled by drought or circadian rhythms. This is probably accomplished through the functioning of Gigantea ( OsGI ). Such findings provide a novel angle for using CrRLK1Ls to study the drought-stress response and circadian regulation. The 17 CrRLK1L members of a novel RLK family have been identified in Arabidopsis. Each carries a putative extracellular carbohydrate-binding malectin-like domain. However, their roles in rice, a widely consumed staple food, are not well understood. To investigate the functions of CrRLK1Ls in rice, we utilized phylogenomics data obtained through anatomical and diurnal meta-expression analyses. This information was integrated with a large set of public microarray data within the context of the rice CrRLK1L family phylogenic tree. Chromosomal locations indicated that 3 of 16 genes were tandem-duplicated, suggesting possible functional redundancy within this family. However, integrated diurnal expression showed functional divergence between two of three genes, i.e., peak expression was detected during the day for OsCrRLK1L2, but during the night for OsCrRLK1L3. We found it interesting that OsCrRLK1L2 expression was repressed in osgigantea (osgi) mutants, which suggests that it could function downstream of OsGI. Network analysis associated with OsCrRLK1L2 and OsGI suggested a novel circadian regulation mechanism mediated by OsGI. In addition, two of five OsCrRLK1Ls preferentially expressed in the roots were stimulated by drought, suggesting a potential role for this family in water-use efficiency. This preliminary identification of CrRLK1Ls and study of their expression in rice will facilitate further functional classifications and applications in plant production.

A measurement of cluster quality is often needed for DNA microarray data analysis. In this paper, we introduce a new cluster validity index, which measures geometrical features of the data. The essential concept of this index is to evaluate the ratio between the squared total length of the data eigen-axes with respect to the between-cluster separation. We show that this cluster validity index works well for data that contain clusters closely distributed or with different sizes. We verify the method using three simulated data sets, two real world data sets and two microarray data sets. The experiment results show that the proposed index is superior to five other cluster validity indices, including partition coefficients (PC), General silhouette index (GS), Dunn’s index (DI), CH Index and I-Index. Also, we have given a theorem to show for what situations the proposed index works well.

In the past years, various microarray technologies have been used to extract useful biological information from microarray data. Microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern, plays an important role in the analysis of genes. The primary techniques involve clustering and biclustering methods. Besides classical clustering methods, biclustering is being preferred to analyze biological datasets, due to its ability to group both genes across conditions simultaneously. Biclustering is being practiced in a number of applications to club genes across specified conditions, used mainly in identifying sets of coregulated genes, tissue classification etc. Gene Ontology is another important area of application, where biclusters are used to presume the class of non-annotated genes. Gene Ontology database is competent of annotating and analyzing a large number of genes. Gene Ontology is a standard approach of representing the gene with their product attributes, across different species and databases. Typical annotations for the analyzed list of genes can be well understood using the BicAT and BiVisu toolbox. The toolbox provides a platform which enables us to compare different biclustering algorithms, inside the graphical tool. This paper compares different biclustering approaches used to analyze carcinoma and DLBCL (diffuse large B-cell lymphoma) microarray datasets. The algorithms were compared on the grounds of enrichment values with support from runtime analysis. The paper explains in detail the biclusters associated with each algorithm and the intellects affecting the enrichment values, leading to the best biclustering technique for the datasets mentioned above.

Control genes, commonly defined as genes that are ubiquitously expressed at stable levels in different biological contexts, have been used to standardize quantitative expression studies for more than 25 yr. We analyzed a group of large mammalian microarray datasets including the NCI60 cancer cell line panel, a leukemia tumor panel, and a phorbol ester induction time course as well as human and mouse tissue panels. Twelve housekeeping genes commonly used as controls in classical expression studies (including GAPD, ACTB, B2M, TUBA, G6PD, LDHA, and HPRT) show considerable variability of expression both within and across microarray datasets. Although we can identify genes with lower variability within individual datasets by heuristic filtering, such genes invariably show different expression levels when compared across other microarray datasets. We confirm these results with an analysis of variance in a controlled mouse dataset, showing the extent of variability in gene expression across tissues. The results show the problems inherent in the classical use of control genes in estimating gene expression levels in different mammalian cell contexts, and highlight the importance of controlled study design in the construction of microarray experiments.

The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data

To evaluate microarray data, clustering is widely used to group biological samples or genes. However, problems arise when comparing heterologous databases. As the clustering algorithm searches for similarities between experiments, it will most likely first separate the data sets, masking relationships that exist between samples from different databases. We developed a program, Venn Mapper, to calculate the statistical significance of the number of co-occurring differentially expressed genes in any of the two experiments. For proof of principle, we analysed a heterologous data set of 170 microarrays including breast and prostate cancer microarray analyses. Significant overlap was found in an unsupervised analysis between metastasized prostate cancer and metastasized breast cancer and BRCA mutated breast cancer. A comparison between single microarray data and the averaged breast and prostate data sets was also evaluated. This analysis suggests that genes expressed higher in stromal cells are also implicated in metastatic prostate cancer and BRCA mutated breast cancer. The Venn Mapper program identifies overlaps between samples from heterologous data sets and directly extracts the genes responsible for the overlap. From this information novel biological hypotheses may be addressed. Venn Mapper is freely available on http://www.erasmusmc.nl/gatcplatform. http://www.erasmusmc.nl/gatcplatform/vennmapper.html.

Microarray experiments are proficient of yielding observations for thousands of genes those are differentially expressed under several conditions. Although it is possible to measure simultaneously the changes in gene expression profiles at whole genomic scale, interpreting individual gene expression profile in terms of its actual biological function or associated biochemical processes remains challenging. Exploratory multivariate statistical techniques such as principal component analysis have been extensively used to reduce the complexity of large size microarray data. Although Saccaromycea Cerevisae is the most widely studied species using microarray techniques, a complete understanding of the efficacy of principal component analysis and data pre-processing is still lacking for clustering and functional mapping of yeast gene expression profiles, reported in various studies. Therefore in this work, we evaluate the impact of data pre-processing and principal component analysis on k-means clustering-based functional mapping of yeast gene expression profiles observed during diauxic-shift. Two time-series gene expression datasets were chosen such as, (1) yeast diauxic-shift data and (2) yeast sporulation data to examine the efficacy of principal component analysis in interpreting gene-based or score-based clusters and their relationship with known pathways. It was shown that unlike conventional pre-processing, principal component analysis provides a powerful tool to capture most of the information using only two component variables for inferring gene expression time-course data. Using yeast genome databases, it was demonstrated that clustering with principal components instead of the original variables does not necessarily improve the cluster quality but helps in identifying the relationships between genes of a cluster and key biological process of diauxic shift. Overall, the present analysis is useful in mining high dimensional microarray data at a reduced computational cost associated with functional enrichment of expression time-series, regardless of species or experimental conditions.