Direct functional assessment of the composite phenotype through multivariate projection strategies.
We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.
Conesa
Ana
A
Bioinformatics Department, Centro de Investigación Principe Felipe, Valencia, Spain.
Bro
Rasmus
R
García-García
Francisco
F
Prats
José Manuel
JM
Götz
Stefan
S
Kjeldahl
Karin
K
Montaner
David
D
Dopazo
Joaquín
J
D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008432:Mathematical Computing; D015999:Multivariate Analysis; D010641:Phenotype