Figure 1

Schematic representation of the proposed methods. a) maSigFun fits a regression model for each gene expression submatrix defined by the genes annotated to a given functional class (FC.1 to 4 in scheme). Significant classes are obtained by the maSigPro method (FC.3). b) PCA-maSigFun obtains a PCA model for the gene expression submatrix defined as in maSigFun and extracts a number of components that collect non-random variation. Generally 0 (FC.1) to 2 (FC.2) components are extracted for each functional class. A regression model is then fitted to the scores vector of extracted components to select function-defined patterns with a significant association to time (FC.2 and FC.3). c) ASCA-functional applies ASCA-genes to identify principal patterns of variation associated with time and time × treatment experimental factors (PC1 to 3 in scheme). Genes are ranked by loading value in each PC, and GSA analysis is applied to each loading value-ordered gene list to identify a functionally related block of genes associated with the principal patterns of variation (FC.2 and FC.3).