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Table of Contents
Development Example
Data:subset_hypoxia_4t_data.zip
Covariates:development_covariates.txt
ASCA-genes
Variability associated to each factor:
- Time 25.86 %
- Treatment+TimexTreatment 37.56 %
- Residuals 36.62 %
Variability explained for each submodel :
- With 2 components, Submodel Time explains 93.66% of variation of factor Time. (Component 1: 49.66% and Component 2: 44%).
- With 2 components, Submodel Treatment+TimexTreatment explains 71.86% of variation of factor Treatment+TimexTreatment.(Component 1: 44.18% and Component 2: 27.67%).
ASCA-Functional
maSigPro
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=0.7 and alpha=0.05 we obtained as significant:
- 1307 genes with changes in the trajectory of A.
- 1158 genes with differences between the trajectories of the groups B and A.
- 1282 genes with differences between the trajectories of the groups C and A.
We represent in 9 groups the trajectories of the second gene-selection (1158 genes).
maSigFun
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:
- GO:0006842 tricarboxylic acid transport
- GO:0010544 negative regulation of platelet activation
- GO:0015746 citrate transport
The trajectory plots of these categories are the following:
PCA-maSigFun
