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examples.development [2010/03/25 13:28] mjnueda |
examples.development [2010/05/05 10:39] (current) mjnueda |
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| ====== Development Example====== | ====== Development Example====== | ||
| - | + | Data:{{:subset_development_4t_data.zip|}} | |
| - | Data:{{:subset_hypoxia_4t_data.zip|}} | + | |
| \\ | \\ | ||
| Covariates:{{:development_covariates.txt|}} | Covariates:{{:development_covariates.txt|}} | ||
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| Variability associated to each factor: | Variability associated to each factor: | ||
| * Time 25.86 % | * Time 25.86 % | ||
| - | * Treatment+TimexTreatment 37.56 % | + | * Treatment+TimexTreatment 37.52 % |
| * Residuals 36.62 % | * Residuals 36.62 % | ||
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| ====== ASCA-Functional ====== | ====== ASCA-Functional ====== | ||
| + | \\ | ||
| + | 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. | ||
| + | \\ | ||
| + | {{:hypoxia_ascafunt.png?600|}} | ||
| + | |||
| ====== maSigPro ====== | ====== 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=0.8 and alpha=0.05 we obtained as significant: | 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: | ||
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| * 1100 categories with differences between the trajectories of the groups B and A. | * 1100 categories with differences between the trajectories of the groups B and A. | ||
| * 1469 categories with differences between the trajectories of the groups C and A. | * 1469 categories with differences between the trajectories of the groups C and A. | ||
| - | |||
| - | maSigVisualization module for PCA-maSigFun offers two outputs: | ||
| - | - Output 1: Trajectory plots of clusters of categories, as in maSigFun. | ||
| - | - Output 2: with the trajectory plots of each category next to a barplot with the correlation of each gene to the displayed trend. | ||
| - | The second output helps to understand the expression profile of the class and the participation of the class members in this profile. | ||
