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[ANGLÈS] A general framework for microarray classification relying on histogram based hierarchical clustering is proposed in this work. It produces precise and reliable classifiers based on a two-step approach. In the first step, the feature set is enhanced by histogram based features corresponding to each cluster produced via hierarchical clustering, where a parameter (maximum number of dominant genes) can be tuned based on the dataset characteristics. In the second step, a reliable classifier is built from a wrapper feature selection process called Improved Sequential Floating Forward Selection (IFFS) to properly choose a small feature set for the classification task. Considering the sample scarcity in the microarray datasets, a reliability parameter has been considered to improve the feature selection process along with classification error rate. Different combinations of error rate and reliability has been used as the scoring rule. Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN) classifiers have been used for this work and the performances has been compared. The potential of the proposed framework has been evaluated with three publicly available datasets : colon, lymphoma and leukaemia. The experimental results have confirmed the usefulness of the histogram based hierarchical clustering and the new representative feature generation algorithm. A gene level analysis has revealed that the best features selected by the feature selection algorithm has only very few basic constituent genes involved. The comparative results showed that the proposed framework can compete with state of the art alternatives. [CASTELLÀ] Un marco general para la clasificación de microarrays se propone en este trabajo. Produce clasificadores precisos y fiables basados en un enfoque de dos pasos. En el primer paso, el conjunto de características se ve reforzado por una serie de características basado en un histograma correspondiente a cada racimo producido a través de la agrupación jerárquica, donde puede ser un parámetro (número máximo de genes dominantes) sintonizado sobre la base de las características del conjunto de datos. En el segundo paso, un clasificador fiable se construye a partir de un proceso de envoltura de la característica de selección llamado Improved Sequential Floating Forward Selection (IFFS) para elegir adecuadamente un conjunto de características pequeño para la tarea de clasificación. Considerando la escasez de la muestra en los microarrays de datos, un parámetro de fiabilidad ha sido considerado para mejorar el proceso de selección de características, junto con la tasa de clasificación de error. Las diferentes combinaciones de tasa de error y la fiabilidad se ha utilizado como la regla de puntuación. Linear Discriminant Analysis (LDA) y K-Nearest Neighbour (KNN) clasificadores se ha utilizado para este trabajo y el rendimiento ha sido comparado. El potencial del proyecto de marco ha sido evaluado con tres conjuntos de datos disponibles al público: colon, linfoma y leucemia. Los resultados experimentales han confirmado la utilidad del histograma basado en la agrupación jerárquica y el algoritmo representante característica nueva generación. Un análisis a nivel de gen ha revelado que las mejores características seleccionadas por el algoritmo de selección de característica sólo tiene genes básicos muy pocos constituyentes implicados. Los resultados comparativos mostraron que el marco propuesto puede competir con el estado del arte de las alternativas. [CATALÀ] Un marc general per a la classificació de microarrays es proposa en aquest treball. Produeix classificadors precisos i fiables basats en un enfocament de dos passos. En el primer pas, el conjunt de característiques es veu reforçada per una sèrie de característiques basat en histograma corresponent a cada raïm produïda a través de l'agrupació jeràrquica, on pot ser un paràmetre (nombre màxim de gens dominants) sintonitzat sobre la base de les característiques del conjunt de dades. En el segon pas, un classificador fiable es construeix a partir d'un procés d'embolcall de la característica de selecció anomenat Improved Sequential Floating Forward Selection (IFFS) per triar adequadament un conjunt de característiques petit per a la tasca de classificació. Considerant l'escassetat de la mostra en els microarrays de dades, un paràmetre de fiabilitat ha estat considerat per millorar el procés de selecció de característiques, juntament amb la taxa de classificació d'error. Les diferents combinacions de taxa d'error i la fiabilitat s'ha utilitzat com la regla de puntuación. Linear Discriminant Analysis (LDA) i K-Nearest Neighbour (KNN) classificadors s'haN utilitzat per aquest treball i el rendiment ha estat comparat. El potencial del projecte de marc ha estat avaluat amb tres conjunts de dades disponibles al públic: còlon, limfoma i leucèmia. Els resultats experimentals han confirmat la utilitat de l'histograma basat en l'agrupació jeràrquica i l'algoritme representant característica nova generació. Una anàlisi a nivell de gen ha revelat que les millors característiques seleccionades per l'algorisme de selecció de característiques només té gens bàsics molt pocs constituents implicats. Els resultats comparatius mostren que el marc proposat pot competir amb l'estat de l'art de les alternatives.

A causal role of mutations in multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations in global levels of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for changes in the overall distribution of gene expression levels. For instance, in mice, variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in variance might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on RNA from peripheral blood lymphocytes (PBL) of children with autism (n = 82) and controls (n = 64). Variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance. A decrease in the variance in the distribution of gene expression levels in PBL was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Global regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders.

Background Despite sharing the same genes, identical twins demonstrate substantial variability in behavioral traits and in their risk for disease. Epigenetic factors–DNA and chromatin modifications that affect levels of gene expression without affecting the DNA sequence–are thought to be important in establishing this variability. Epigenetically-mediated differences in the levels of gene expression that are associated with individual variability traditionally are thought to occur only in a gene-specific manner. We challenge this idea by exploring the large-scale organizational patterns of gene expression in an epigenetic model of behavioral variability. Methodology/Findings To study the effects of epigenetic influences on behavioral variability, we examine gene expression in genetically identical mice. Using a novel approach to microarray analysis, we show that variability in the large-scale organization of gene expression levels, rather than differences in the expression levels of specific genes, is associated with individual differences in behavior. Specifically, increased activity in the open field is associated with increased variance of log-transformed measures of gene expression in the hippocampus, a brain region involved in open field activity. Early life experience that increases adult activity in the open field also similarly modifies the variance of gene expression levels. The same association of the variance of gene expression levels with behavioral variability is found with levels of gene expression in the hippocampus of genetically heterogeneous outbred populations of mice, suggesting that variation in the large-scale organization of gene expression levels may also be relevant to phenotypic differences in outbred populations such as humans. We find that the increased variance in gene expression levels is attributable to an increasing separation of several large, log-normally distributed families of gene expression levels. We also show that the presence of these multiple log-normal distributions of gene expression levels is a universal characteristic of gene expression in eurkaryotes. We use data from the MicroArray Quality Control Project (MAQC) to demonstrate that our method is robust and that it reliably detects biological differences in the large-scale organization of gene expression levels. Conclusions Our results contrast with the traditional belief that epigenetic effects on gene expression occur only at the level of specific genes and suggest instead that the large-scale organization of gene expression levels provides important insights into the relationship of gene expression with behavioral variability. Understanding the epigenetic, genetic, and environmental factors that regulate the large-scale organization of gene expression levels, and how changes in this large-scale organization influences brain development and behavior will be a major future challenge in the field of behavioral genomics.

Background It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported. Results We introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License. Conclusion The availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from http://microarray.cu.genome.org.

Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. Results: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. Conclusion: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Background: We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression. Results: Our analysis revealed many additional genes that might be involved in behavioral differences between the two mouse strains and functional differences between the six brain regions. Using conservative statistical criteria, we identified at least 63 genes showing strain variation and approximately 600 genes showing regional variation. Unlike ad hoc methods, ours have the additional benefit of ranking the genes by statistical score, permitting further analysis to focus on the most significant. Comparison of our results to the previous studies and to published reports on individual genes show that we achieved high sensitivity while preserving selectivity. Conclusions: Our results indicate that molecular differences between the strains and regions studied are larger than indicated previously. We conclude that for large complex datasets, ANOVA and feature selection, alone or in combination, are more powerful than methods based on fold-change thresholds and other ad hoc selection criteria.

Background DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously. Results BAMarray™ is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarray™ can also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray™ provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses. Conclusion BAMarray™ is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray™ is licensed software freely available to academic institutions. More information can be found at http://www.bamarray.com.

Our laboratory is examining the profiles of microRNA expression in ATLL cells and infected T-cell lines using microarrays and small RNA libraries. Microarray analysis of ATLL samples revealed 6 upregulated and 21 downregulated microRNAs in ATLL cells compared to CD4+ T-cell controls. Potential targets for deregulated microRNAs were identified by integrating microRNA and mRNA expression profiles. Current experiments are aimed at verifying these predicted microRNA-target interactions. Mass sequencing of small RNA libraries prepared from normal CD4+ cells and two chronically infected T-cell lines yielded panels of known and candidate new microRNAs for each library. Comparison of frequencies of known microRNAs led to the identification of a small number of microRNAs differentially expressed in both infected cell lines compared to controls. Most of the candidate new microRNAs were intragenic with poor species conservation, suggesting that they might have particular roles in human T-cell function. Two sequences mapped to the HTLV-1 genome, suggesting that the virus may produce its own microRNAs. Further analyses of the new cellular and viral microRNA candidates are in progress.