MANOVA

Radlinskeho 9, 81237 Bratislava, Slovakia, e-mail alena.manova stuba.sk... 

Manova, K., and Bachvarova, R. F. (1991). Expression of c-kit encoded at the W locus of mice in developing embryonic germ cells and presumptive melanoblasts. Dev. Biol. 146 312-324. 

Kiyokawa H, Kineman RD, Manova-Todorova KO et al 1996 Enhanced growth of mice lacking the cyclin-dependent kinase inhibitor function of p27(Kipl). Cell 85 721—732 KondoT, RaffM 2000a The Id4 HLH protein and the timing of oligodendrocyte differentiation. EMBO J 19 1998-2007... 

Darder, M., Aranda, P., Hernandez-Velez, M., Manova, E. and Ruiz-Hitzky, E. (2006) Thin Solid Films, 495, 321-326. 

Multivariate analysis of variance-covariance (MANOVA/MANCOVA) techniques. 

Fernandez-Capetillo O, Mahadevaiah SK, Celeste A, Romanienko PJ, Camerini-Otero RD, Bonner WM, Manova K, Burgoyne P, Nussenzweig A (2003c) H2AX is required for chromatin remodeling and inactivation of sex chromosomes in male mouse meiosis. Dev Cell 4 497—508... 

Grinstein E, Wernet P, Snijders PJ, Rosl F, Weinert I, Jia W, Kraft R, Schewe C, Schwabe M, Hauptmann S, Dietel M, Meijer CJ, Royer HD (2002) Nucleolin as activator of human papillomavirus type 18 oncogene transcription in cervical cancer. J Exp Med 196 1067-1078 Grisendi S, Bernard R, Rossi M, Cheng K, Khandker L, Manova K, Pandolfi PP (2005) Role of nucleophosmin in embryonic development and tumorigenesis. Nature 437 147-153 Hanakahi LA, Bu Z, Maizels N (2000) The C-terminal domain of nucleolin accelerates nucleic acid annealing. Biochemistry 39 15493-15499... 

Krainev AG, Weiner LM, Kondrashin SK, Kanaeva IP, Bach-manova GI. 1991. Substrate access channel geometry of soluble and membrane-bound cytochromes P450 as studied by interactions with type II substrate analogues. Arch Biochem Biophys 288 17-21. 

Kiyokawa, H., Kineman, R., Manova-Todorova, K., Soares, V., Hoffman, E., Onoi, M., Hayday, A., Frohman, D., and Koff, A. (1996). Enhancehd growth of mice lacking the cyclin-dependent kinase inhibitor function of p27Kipl, Cell 85, 721-732. 

Equally important is the use of suitable quantitative statistical analyses, including more complicated models, because they can hold certain variables constant, control for artifacts, and provide supplementary information. Whatever statistics are used, they should be explicitly described in sufficient detail so the reader knows exactly what was done and can make a judgment about their appropriateness. For example, there are many different types of analyses of variance (ANOVA), analyses of covariance (ANCOVA), or multivariate analyses of variance (MANOVA), and some are not appropriate to the task at hand. If only the results of an ANOVA with a p < 0.001 are provided, the reader should be justifiably dubious, because this model may not be proper ( p is an estimate of the probability that the results occurred by chance). Sufficient details are required to clarify which model was used, because the p value may be invalid with an inappropriate model. 

Damborsky, J., K. Manova, and M. Kuty, A mechanistic approach to deriving quantitative structure biodegradability relationships. A case study Dehalogenation of haloaliphatic compounds . In Biodegradability Prediction, W. J. G. M. Peijnenburg and J. Damborsky, Eds., Kluwer Academic, Dordrecht, 1996. 

The simultaneous comparison of the mean values of a set of features, i.e. two vectors of means in the simplest case of two classes, we will call multivariate analysis of variance (MANOVA). 

Fig. 7-5. Plot of the scores of discriminant function df 2 vs. scores of discriminant function df 1 of the different emission impact monitoring raster screens I, II and the emission impact sampling point of town III [(o) town I, ( ) town II, ( ) town III], (The circles correspond to the 5% risk of error of the MANOVA)...

The MANOVA enables significant class separation with a multivariate scaled separation measure of 330.9. The sampling times 5 a.m. and 11 p.m. are well separable from the times 11 a.m. and 5 p.m. by the optimum separation set which consists in the features suspended material, iron, magnesium, nickel, and copper. The result of discriminant analysis is shown in the plane of the two strongest discriminant functions (Fig. 8-3). 

The principle of multivariate analysis of variance and discriminant analysis (MVDA) consists in testing the differences between a priori classes (MANOVA) and their maximum separation by modeling (MDA). The variance between the classes will be maximized and the variance within the classes will be minimized by simultaneous consideration of all observed features. The classification of new objects into the a priori classes, i.e. the reclassification of the learning data set of the objects, takes place according to the values of discriminant functions. These discriminant functions are linear combinations of the optimum set of the original features for class separation. The mathematical fundamentals of the MVDA are explained in Section 5.6. 

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