Multivariate Analysis in Phytopathology: Options and opportunities in data mining to face new molecular information.

BALZARINI, MÓNICA; BRUNO, CECILIA; FERNÁNDEZ, ELMER ANDRÉS

Phytopathology in the omics era

Singpost Research

Lugar: Mexico; Año: 2011;

Multivariate techniques and machine learning methodsare used by phytopathology scientists working with highlydimensional genomic and proteomic data. Molecular data are oftenobtained by high throughput technologies, so the amount of datagenerated is huge and it demands an overall process of finding andinterpreting patterns from data where statistical and bioinformatictechniques are crucial. When properly analyzed, molecular markerdata allow biological meaningful reflection of DNA and RNAcomparisons between individuals. An accurate interpretation ofsuch type of omic data depends on understanding the strengths andlimitations of the available analytic methodologies for processingmolecular information. In this chapter we describe a number ofstatistical methods, multivariate in nature, used with molecular datafor ordination and classification of molecular data in reducedspaces, give brief illustration of applications in phytopathology, and discuss how they can be used in complementary manner no only in cross-sectional butalso in spatial genomics. We talk about methods of ordination and classification thatwere too difficult to implement in the near past because their high computing demandbut which are now easily to use due to the increase in computing capabilities.