Thu, 28 Aug 2008 04:31:44 BST CiteULike: de vrich analysis http://www.citeulike.org/user/vrich/tag/analysis CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/vrich/article/1005974 <i>Bioinformatics, Vol. 22, No. 15. (1 August 2006), pp. 1924-1925.</i><br /><br />MOTIVATION: An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria. AVAILABILITY: The Comparative Marker Selection application suite is freely available as a GenePattern module. The GenePattern analysis environment is freely available at http://www.broad.mit.edu/genepattern. Comparative gene marker selection suite. J Gould G Getz S Monti M Reich JP Mesirov doi:10.1093/bioinformatics/btl196 Bioinformatics, Vol. 22, No. 15. (1 August 2006), pp. 1924-1925. 2006-12-21T15:30:00-00:00 2006 Bioinformatics 1460-2059 22 15 1924 1925 analysis gene_pattern stats http://www.citeulike.org/user/vrich/article/2066845 <i>FEMS Microbiology Ecology, Vol. 62, No. 2. (2007), pp. 142-160.</i><br /><br />Abstract Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology. Multivariate analyses in microbial ecology Alban Ramette doi:10.1111/j.1574-6941.2007.00375.x FEMS Microbiology Ecology, Vol. 62, No. 2. (2007), pp. 142-160. 2007-12-06T13:07:58-00:00 2007 FEMS Microbiology Ecology 62 2 142 160 analysis stats http://www.citeulike.org/user/vrich/article/2746821 <i>The American Statistician, Vol. 46, No. 3. (1992), pp. 217-225.</i><br /><br />Canonical discriminant functions are defined here as linear combinations that separate groups of observations, and canonical variates are defined as linear combinations associated with canonical correlations between two sets of variables. In standardized form, the coefficients in either type of canonical function provide information about the joint contribution of the variables to the canonical function. The standardized coefficients can be converted to correlations between the variables and the canonical function. These correlations generally alter the interpretation of the canonical functions. For canonical discriminant functions, the standardized coefficients are compared with the correlations, with partial t and F tests, and with rotated coefficients. For canonical variates, the discussion includes standardized coefficients, correlations between variables and the function, rotation, and redundancy analysis. Various approaches to interpretation of principal components are compared: the choice between the covariance and correlation matrices, the conversion of coefficients to correlations, the rotation of the coefficients, and the effect of special patterns in the covariance and correlation matrices. Interpretation of Canonical Discriminant Functions, Canonical Variates, and Principal Components Alvin Rencher doi:10.2307/2685219 The American Statistician, Vol. 46, No. 3. (1992), pp. 217-225. 2008-05-02T18:54:10-00:00 1992 The American Statistician 46 3 217 225 American Statistical Association analysis cda stats http://www.citeulike.org/user/vrich/article/2621415 <i>Proceedings of the National Academy of Sciences, Vol. 105, No. 13. (1 April 2008), pp. 4972-4975.</i><br /><br />In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view. 10.1073/pnas.0709247105 From time series to complex networks: The visibility graph Lucas Lacasa Bartolo Luque Fernando Ballesteros Jordi Luque Juan Nuno doi:10.1073/pnas.0709247105 Proceedings of the National Academy of Sciences, Vol. 105, No. 13. (1 April 2008), pp. 4972-4975. 2008-04-02T00:15:03-00:00 2008 Proceedings of the National Academy of Sciences 105 13 4972 4975 analysis