3 resultados para Multivariate data


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Nuevas biotecnologías permiten obtener información para caracterizar materiales genéticos a partir de múltiples marcadores, ya sean éstos moleculares y/o morfológicos. La ordenación del material genético a través de la exploración de patrones de variabilidad multidimensionales se aborda mediante diversas técnicas de análisis multivariado. Las técnicas multivariadas de reducción de dimensión (TRD) y la representación gráfica de las mismas cobran sustancial importancia en la visualización de datos multivariados en espacios de baja dimensión ya que facilitan la interpretación de interrelaciones entre las variables (marcadores) y entre los casos u observaciones bajo análisis. Tanto el Análisis de Componentes Principales, como el Análisis de Coordenadas Principales y el Análisis de Procrustes Generalizado son TRD aplicables a datos provenientes de marcadores moleculares y/o morfológicos. Los Árboles de Mínimo Recorrido y los biplots constituyen técnicas para lograr representaciones geométricas de resultados provenientes de TRD. En este trabajo se describen estas técnicas multivariadas y se ilustran sus aplicaciones sobre dos conjuntos de datos, moleculares y morfológicos, usados para caracterizar material genético fúngico.

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Measures of agro-ecosystems genetic variability are essential to sustain scientific-based actions and policies tending to protect the ecosystem services they provide. To build the genetic variability datum it is necessary to deal with a large number and different types of variables. Molecular marker data is highly dimensional by nature, and frequently additional types of information are obtained, as morphological and physiological traits. This way, genetic variability studies are usually associated with the measurement of several traits on each entity. Multivariate methods are aimed at finding proximities between entities characterized by multiple traits by summarizing information in few synthetic variables. In this work we discuss and illustrate several multivariate methods used for different purposes to build the datum of genetic variability. We include methods applied in studies for exploring the spatial structure of genetic variability and the association of genetic data to other sources of information. Multivariate techniques allow the pursuit of the genetic variability datum, as a unifying notion that merges concepts of type, abundance and distribution of variability at gene level.

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This work is a multidisciplinary environmental study that provides new insights into the relationships between sediment-organic matter characteristics and polybrominated diphenyl ethers (PBDEs) concentration. The aim of the present multivariate study was to correlate factors influencing PBDEs accumulation in sediment by using principal component analysis (PCA). Organic matter studies by Fourier Transform-Infrared spectroscopy and physicochemical analyses (Total Organic Carbon, pH, electrical conductivity) of sediment samples were considered for PCA. Samples were collected from an artificial irrigation network on the Mendoza River irrigation areas. PCA provided a comprehensive analysis of the studied variables, identifying two components that explained 63% of the data variance. Those factors were mainly associated to organic matter degradation degree, which represent a new insight into the relationships between organic matter in sediments and PBDEs fate. In this sense it was possible to determine that not only the content but also the type of organic matter (chemical structure) could be relevant when evaluating PBDEs accumulation and transport in the environment. Typification of organic matter may be a useful tool to predict more feasible areas where PBDE, may accumulate, as well as sediment transportation capability.