ABSTRACT
La diversidad genómica, expresada en las diferencias entre haplotipos moleculares de un conjunto de individuos, puede dividirse en componentes de variabilidad entre y dentro de algún factor de clasificación de los individuos. Para tal partición de varianzas, se usa análisis molecular de la varianza (AMOVA), el cual se construye a partir de las distancias multivariadas entre pares de haplotipos. El AMOVA clásico permite evaluar la significancia estadística de dos o más factores jerárquicos y consecuentemente no existe prueba de interacción entre factores. Sin embargo, existen situaciones donde los factores que clasifican a los individuos están cruzados y no anidados, es decir todos los niveles de un factor se encuentran representados en cada nivel del otro factor. Este trabajo propone una prueba estadística para evaluar la interacción entre factores cruzados en un AMOVA No-Jerárquico. La hipótesis nula de interacción establece que las diferencias moleculares entre individuos de distintos niveles de un factor son las mismas para todos los niveles del otro factor que los clasifica. La propuesta de análisis de interacción de factores a partir de distancias en un AMOVA No-Jerárquico comprende: cálculo de la matriz de distancia y partición de la misma en bloques, posterior cálculo de residuos y análisis de varianza no-paramétrico sobre los residuos. Su implementación es ilustrada en escenarios simulados y real. Los resultados sugieren que la prueba de interacción propuesta para el AMOVA No- Jerárquico presenta alta potencia.
The genomic diversity, expressed in the differences between molecular haplotypes of a group of individuals, can be divided into components of variability between and within some factor of classification of the individuals. For such variance partitioning, molecular analysis of variance (AMOVA) is used, which is constructed from the multivariate distances between pairs of haplotypes. The classical AMOVA allows the evaluation of the statistical significance of two or more hierarchical factors and consequently there is no interaction test between factors. However, there are situations where the factors that classify individuals are crossed rather than nested, that is, all the levels of a factor are represented in each level of the other one. This paper proposes a statistical test to evaluate the interaction between crossed factors in a Non-Hierarchical AMOVA. The null hypothesis of interaction establishes that the molecular differences between individuals of different levels of a factor are the same for all the levels of the other factor that classifies them. The proposed analysis of interaction in a Non- Hierarchical AMOVA includes: calculation of the distance matrix and partition of it into blocks, subsequent calculation of residuals and analysis of non-parametric variance on the residuals. Its implementation is illustrated in simulated and real scenarios. The results suggest that the proposed interaction test for the Non-Hierarchical AMOVA presents high power.
ABSTRACT
This review article presents an assessment of trends in statistical methods and an evaluation of their appropriateness in articles published in the Archives of Plastic Surgery (APS) from 2012 to 2017. We reviewed 388 original articles published in APS between 2012 and 2017. We categorized the articles that used statistical methods according to the type of statistical method, the number of statistical methods, and the type of statistical software used. We checked whether there were errors in the description of statistical methods and results. A total of 230 articles (59.3%) published in APS between 2012 and 2017 used one or more statistical method. Within these articles, there were 261 applications of statistical methods with continuous or ordinal outcomes, and 139 applications of statistical methods with categorical outcome. The Pearson chi-square test (17.4%) and the Mann-Whitney U test (14.4%) were the most frequently used methods. Errors in describing statistical methods and results were found in 133 of the 230 articles (57.8%). Inadequate description of P-values was the most common error (39.1%). Among the 230 articles that used statistical methods, 71.7% provided details about the statistical software programs used for the analyses. SPSS was predominantly used in the articles that presented statistical analyses. We found that the use of statistical methods in APS has increased over the last 6 years. It seems that researchers have been paying more attention to the proper use of statistics in recent years. It is expected that these positive trends will continue in APS.
Subject(s)
Methods , Plastics , Surgery, PlasticABSTRACT
This work constituted a significant contribution for more efficient use of a valuable computer program of non-parametric fitting of nonlinear multiparametric equations to experimental data. However, prerequisite in this context was the transformation of nonlinear multiparametric equations into linear hyperplane forms before their incorporation within the computer program; this latter was decisive and a matter of proper programming practice. Herein, a series of widely used equations useful in different fields of chemical processes, in biochemistry and/or in biotechnology, along with their suitable transformations as well as the appropriate programming support are being reported.
Este trabalho constitui uma contribuição significativa para um uso mais eficiente de um programa de computador valioso do encaixe não-paramétrico de equações multiparamétricas não lineares aos dados experimentais. Entretanto, o pré-requisito neste contexto é a transformação de equações multiparamétricas não lineares em formulários lineares do hyperplano antes de sua incorporação dentro do programa de computador; este último é decisive e uma matéria da prática de programação apropriada. Nisto, nós relatamos uma série das equações extensamente usadas úteis em campos diferentes de processos químicos, em bioquímia e/ou em biotechnologia, junto com suas transformações apropriadas além a sustentação de programação apropriada.