02659nas a2200253   4500008004100000245010800041210006900149300001000218490000700228520165100235653006101886653019201947100001402139700001302153700001302166700001802179700001702197700001502214700002002229700001602249700001502265700001902280856010602299       2008                        eng d00aCLEAR-test: combining inference for differential expression and variability in microarray data analysis0 aCLEARtest combining inference for differential expression and va  a33-450 v413 a<p>A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.</p>10a*Algorithms Artificial Intelligence *Data Interpretation10aStatistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology1 aValls, J.1 aGrau, M.1 aSole, X.1 aHernandez, P.1 aMontaner, D.1 aDopazo, J.1 aPeinado, M., A.1 aCapella, G.1 aMoreno, V.1 aPujana, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1759700902784nas a2200301   4500008004100000245006900041210006800110300000800178490000600186520165900192653002501851653002201876653001901898653006101917653007001978653003402048653013602082100001802218700002102236700001702257700002402274700001402298700001402312700001602326700001502342700001902357856010602376       2007                        eng d00aEvidence for systems-level molecular mechanisms of tumorigenesis0 aEvidence for systemslevel molecular mechanisms of tumorigenesis  a1850 v83 aBACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.10a*Cell Transformation10aBiological Models10aGenetic Models10aMessenger/metabolism Signal Transduction Systems Biology10aNeoplastic *Gene Expression Profiling *Gene Expression Regulation10aNeoplastic Humans Male Models10aStatistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA1 aHernandez, P.1 aHuerta-Cepas, J.1 aMontaner, D.1 aAl-Shahrour, Fatima1 aValls, J.1 aGomez, L.1 aCapella, G.1 aDopazo, J.1 aPujana, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1758491501882nas a2200313   4500008004100000245005200041210005100093300001200144490000700156520100300163653001001166653002901176100001701205700001601222700002101238700001601259700002301275700001401298700001601312700001301328700001801341700001401359700001901373700001501392700001601407700002401423700001501447856010601462       2006                        eng d00aNext station in microarray data analysis: GEPAS0 aNext station in microarray data analysis GEPAS  aW486-910 v343 a<p>The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.</p>10agepas10amicroarray data analysis1 aMontaner, D.1 aTarraga, J.1 aHuerta-Cepas, J.1 aBurguet, J.1 aVaquerizas, J., M.1 aConde, L.1 aMinguez, P.1 aVera, J.1 aMukherjee, S.1 aValls, J.1 aPujana, M., A.1 aAlloza, E.1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16845056