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Development Example

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=0.8 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).


?500

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

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:

  • 1466 categories with changes in the trajectory of 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.

maSigVisualization module for PCA-maSigFun offers two outputs:

  1. Output 1: Trajectory plots of clusters of categories, as in maSigFun.
  2. Output 2:A pdf file 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.

examples.development.1269520083.txt.gz · Last modified: 2010/03/25 13:28 by mjnueda
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