GRID distribution supports clustering validation of large mixed microarray data sets

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.

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.

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.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large ( = 1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).

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

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.

Breast cancer is the leading cause of death for women between the ages of 35 to 65. This is mostly due to intertumor heterogeneity and the lack of specific therapies for all subtypes. However, some breast cancers with an unexpected good prognosis are associated with enhanced antitumor immunity in situ. We studied whether breast cancer subtypes might have different susceptibilities to natural killer (NK) cells' antitumor immunity. We collected a large public set of microarray data for primary breast tumors and determined NK cell ligand expression. We found that despite heterogeneous levels of inhibitory HLA members, NKG2D ligands and DNAM ligands are expressed in virtually all breast tumor subtypes. Functional experiments in breast cancer subtypes expressing various levels of NK cell ligands showed that NK-mediated cytotoxicity is mainly HLA, NKG2D, and DNAM dependent. In parallel, we showed that cell lines and primary breast tumor cells secrete soluble inhibitory factors that alter NK cell functions. Finally, we showed that these mechanisms of escape occur in vivo in the MMTV-Neu model of spontaneous murine breast cancer. Our study shows that breast cancer cells, independent of the subtype, have developed different mechanisms to escape from NK cells' antitumor immunity. These results emphasize the role of NK cells in breast tumor clearance and underlie the importance of devising future therapy aiming at enhancing NK cell-mediated recognition in parallel with the prevention of the tumor-editing process.

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.

BackgroundThe extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states.ResultsIn this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions.ConclusionThe proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

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

ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA on the individual variance components. Interaction effects can be visualized by biplots, showing genes and variables in one plot, providing insight in the effect of e.g. treatment or time on gene expression. Because ANOVA has removed uninteresting sources of variance, the results are much more interpretable than without ANOVA. Moreover, the combination of ANOVA and PCA provides a simple way to select genes, based on the interactions of interest. It is shown that the components from an ANOVA model can be summarized and visualized with PCA, which improves the interpretability of the models. The method is applied to a real time-course gene expression dataset of mesenchymal stem cells. The dataset was designed to investigate the effect of different treatments on osteogenesis. The biplots generated with the algorithm give specific information about the effects of specific treatments on genes over time. These results are in agreement with the literature. The biological validation with GO annotation from the genes present in the selections shows that biologically relevant groups of genes are selected. R code with the implementation of the method for this dataset is available from http://www.cac.science.ru.nl under the heading "Software".

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.

Chapter 5 - The Purpose of Data Analysis Is to Enable Data Reanalysis

<div>Abstract<p>Breast cancer is the leading cause of death for women between the ages of 35 to 65. This is mostly due to intertumor heterogeneity and the lack of specific therapies for all subtypes. However, some breast cancers with an unexpected good prognosis are associated with enhanced antitumor immunity <i>in situ</i>. We studied whether breast cancer subtypes might have different susceptibilities to natural killer (NK) cells' antitumor immunity. We collected a large public set of microarray data for primary breast tumors and determined NK cell ligand expression. We found that despite heterogeneous levels of inhibitory HLA members, NKG2D ligands and DNAM ligands are expressed in virtually all breast tumor subtypes. Functional experiments in breast cancer subtypes expressing various levels of NK cell ligands showed that NK-mediated cytotoxicity is mainly HLA, NKG2D, and DNAM dependent. In parallel, we showed that cell lines and primary breast tumor cells secrete soluble inhibitory factors that alter NK cell functions. Finally, we showed that these mechanisms of escape occur <i>in vivo</i> in the MMTV-Neu model of spontaneous murine breast cancer. Our study shows that breast cancer cells, independent of the subtype, have developed different mechanisms to escape from NK cells' antitumor immunity. These results emphasize the role of NK cells in breast tumor clearance and underlie the importance of devising future therapy aiming at enhancing NK cell–mediated recognition in parallel with the prevention of the tumor-editing process. <i>Cancer Res; 71(21); 6621–32. ©2011 AACR</i>.</p></div>