DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. Association rule mining and loglinear models have been used for this purpose, but their inherent limitations as well as information loss due to discretization limit the applicability of the results. Here we propose the use of a Graphical Gaussian Model to discover pairwise gene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships; results can be validated by experts or known information, or suggest new experiments. We have tested our methodology using the yeast microarray data. Our results reveal some previously unknown interactions that have solid biological explanations.

Supervised group Lasso with applications to microarray data analysis

At present, there is a lack of a sound methodology to infer causal gene expression relationships on a genome wide basis. We address this first by examining the behaviour of some of the latest and fastest algorithms for tree and cluster analysis, particularly hierarchical methods popular in phylogenetics. Combined with these are two novel distances based on partial, rather than full, correlations. Theoretically, partial correlations should provide better evidence for regulatory genetic links than standard correlations. To compare the clusters obtained by many alternative methods we use tree consensus methods. To compare methods of analysis we used tree partition metrics followed by another level of clustering. These, and a tree fit metric, all suggest that the new distances give quite different trees than those usually obtained. In the second part we consider graphical modeling of the interactions of important genes of the cell cycle. Despite the models seeming to fit well on occasions, and despite the experimental error structure seeming close to multivariate normal, there are considerable problems to overcome. Latent variables, in this case important genes missing from the analysis, are inferred to have a strong effect on the partial correlations. Also, the data show clear evidence of sampling distributions conditional on the status of important cancer related genes, including TP53. Without full information on which genes are wild type the appropriate models cannot be fitted. These findings point to the need to include and distinguish not only all relevant genes but also all splice variants in the design phase of a microarray analysis. Failure to do so will induce problems similar to both latent variables and conditional distributions.

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DNA Microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. In this paper, we propose to use graphical modeling based interaction analysis for this purpose. We apply graphical gaussian model to discover pairwise gene interactions and use loglinear model to discover multi-gene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships; results can be validated by experts or known information, or suggest new experiments. We have tested our methodology using the yeast microarray data. Our results reveal some previously unknown interactions that have solid biological explanations.

Inflammation is an inherent response of the organism that permits its survival despite constant environmental challenges. The process normally leads to recovery from injury and to healing. However, if targeted destruction and assisted repair are not properly phased, chronic inflammation can result in persistent tissue damage. To better understand the inflammatory process, we recently introduced a profiling methodology to identify common genes involved in bladder inflammation. The method represents a complementation to the classical quantification of inflammation and provides information regarding the early, intermediate, and late events in gene regulation. However, geneprofiling fails to describe the molecular pathways and their interconnections involved in the particular inflammatory response. The present work introduces a new statistic technique for inferring functional interconnections between inflammatory pathways underlying classical models of bladder inflammation and permits the modeling of the inflammatory network. This new statistical

Systematic interpretation of microarray data using experiment annotations

We compared microarray experiments on cell cycle of three model eukaryotes: budding and fission yeast and human cells. Only 112 orthologous groups were cyclic in the three model organisms. The common set of cyclic orthologs includes many taking part in the cell cycle progression, like cyclin B homologs, CDC5, SCH9, DSK2, ZPR1. Proteins involved in DNA replication included histones, some checkpoint kinases and some proteins regulating DNA damage and repair. Conserved cyclic proteins involved in cytokinesis included myosins and kinesins. Many groups of genes related to translation and other metabolic processes were also cyclic in all three organisms. This reflects rebuilding of cellular components after the replication and changes of metabolism during the cell cycle. Many genes important in cell cycle control are not cyclic or not conserved. This includes transcription factors implicated in the regulation of budding yeast cell cycle. The partially overlapping roles of regulatory proteins might allow the evolutionary substitution of components of cell cycle.

We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties and weak transitivity to the dependencies used in the construction of G and the independencies obtained from G by vertex separation. We argue that assuming weak transitivity is not too restrictive. As an intermediate step in the derivation of the graphical criterion, we prove that for any undirected graph G there exists a strictly positive discrete probability distribution with the prescribed sample spaces that is faithful to G. We also report an algorithm that implements the graphical criterion and whose running time is considered to be at most O(n 2 (e+n)) for n nodes and e edges. Finally, we illustrate how the graphical criterion can be used within bioinformatics to identify biologically meaningful gene dependencies.

Abstract: We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from an n-sample of a Gaussian law PC in R p and focus on the disadvantageous case where n is smaller than p. To estimate the graph of conditional dependences of PC, we introduce a collection of candidate graphs and then select one of them by minimizing a penalized empirical risk. Our main result assesses the performance of the procedure in a non-asymptotic setting. We pay special attention to the maximal degree D of the graphs that we can handle, which turns to be roughly n/(2 log p).