Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Cómo aplicar las pruebas paramétricas bivariadas t de Student y ANOVA en SPSS.

Las pruebas paramétricas son un tipo de pruebas de significación estadística que cuantifican la asociación o independencia entre una variable cuantitativa y una categórica. Las pruebas paramétricas exigen ciertos requisitos previos para su aplicación: la distribución Normal de la variable cuantitativa en los grupos que se comparan, la homogeneidad de varianzas en las poblaciones de las que proceden los grupos y una n muestral no inferior a 30. Su incumplimiento conlleva la necesidad de recurrir a pruebas estadísticas no paramétricas. Las pruebas paramétricas se clasifican en dos: prueba t (para una muestra o para dos muestras relacionadas o independientes) y prueba ANOVA (para más de dos muestras independientes).

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

C??mo aplicar las pruebas param??tricas bivariadas 't' de Student y ANOVA en SPSS : caso pr??ctico

Resumen tomado de la publicaci??n

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

Estadística. Problemas resueltos. Análisis de la Varianza (ANOVA) de un factor y test a posteriori

Ejercicio resuelto paso a paso sobre el contraste ANOVA de un factor y contrastes a posteriori.

Estadística Aplicada a los Recursos Marinos. ANOVA multifactorial. Diseños ortogonales

Tema: Diseños con Factores Ortogonales

Trends in Socioeconomic Inequalities in Ischemic Heart Disease Mortality in Small Areas of Nine Spanish Cities from 1996 to 2007 Using Smoothed ANOVA

The aim of this study was to analyze the evolution of socioeconomic inequalities in mortality due to ischemic heart diseases (IHD) in the census tracts of nine Spanish cities between the periods 1996–2001 and 2002–2007. Among women, there are socioeconomic inequalities in IHD mortality in the first period which tended to remain stable or even increase in the second period in most of the cities. Among men, in general, no socioeconomic inequalities have been detected for this cause in either of the periods. These results highlight the importance of intra-urban inequalities in mortality due to IHD and their evolution over time.

Estadística Aplicada a los Recursos Marinos. Permanova. Análisis de Permutaciones y Multiple ANOVA

Análisis multivariante con técnicas de Permutaciones y MANOVA (Permanova)

Curso de ANOVA Multifactorial - Tema 1 - ANOVA Intro

Introducción y conceptos de ANOVA

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.