Generic framework for high-dimensional fixed-effects ANOVA.
Brief Bioinform
; 13(5): 524-35, 2012 Sep.
Article
en En
| MEDLINE
| ID: mdl-22199378
ABSTRACT
In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Análisis de Varianza
/
Genómica
/
Metabolómica
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Países Bajos