Data:subset_development_4t_data.zip
Covariates:development_covariates.txt
Variability associated to each factor:
Variability explained for each submodel :
By applying ASCA-Functional to the first component of the submodel Treatment+TimexTreatment and considering a significant level of 0.05 in the GSA analysis we detected as significant 13 functional categories.
The categories with an adjusted p-value less than 0.05 appear in the first two tables of the ASCA-Functional results.
General trends showed with ASCA-genes module suggest that a quadratic model can be adequate to study gene expression evolution. By applying maSigPro with degree=2 (the quadratic model), R-squared=0.8 and alpha=0.05 we obtained as significant:
We represent in 9 groups the trajectories of the second gene-selection (1158 genes).
By applying maSigFun with degree=2, R-squared=0.4, alpha=0.05 and annotations GO biological process of Human organism we selected as significant the following categories:
Finally we run PCA-maSigFun with degree=2, R-squared=0.8 and alpha=0.05. This tool selected as statistically significant the following number of categories for the experimental groups: