ASCA-functional is a GSA strategy for multifactorial gene expression experiments based on ASCA modeling [3]. ASCA is a multivariate technique that combines ANOVA and PCA to analyse multifactorial experiments. The method identifies major trajectory changes in serial transcriptomics data as the principal components of the PCA decomposition of the transcriptional signal associated to the experimental factors. In ASCA-functional [2] ranks of genes are obtained according the gene loadings at the selected components of the ASCA-models and the partitioning GSA approach FatiScan [5] is applied to this ranking. The loading is a measure of the similarity of each particular gene expression profile to the pattern depicted by the component of the ASCA-model. The program returns a list of significant functional categories associated to the major serial gene expression patterns contained in the data.

Parameters:

• Data: txt file with expression data, genes in rows, arrays in columns. The file must contain an additional row with arrays names and a column with gene names.
  

• Covariates: txt file with experimental design information, containing as many columns as arrays and as many rows as experimental factor. Each cell contains the value of the array in the experimental factor. E.g:

• Include interaction between factors: logical to indicate whether interaction/s between factors should be analyzed (only for experimental designs with more than one factor).
  
• Join interaction with second factor: logical to indicate whether interaction/s must be analysed jointly with the second/third factor (TRUE, default) or not (FALSE).
  
• Variability threshold for component selection: Criterion for selection of principal components. With “average”, components are selected that explain more than the average component variability, calculated as total data variability divided by the rank of the matrix associated to the factor. Also a fixed value for percentage of explained variability can be indicated such as 0.2, 0.4, 0.5, 0.7, 0.8…