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Multiple comparisons can be computed in one GSA run,; Figure 5 is showing the above three comparisons are added in the computation. Image Removed
Figure 5:

In terms of design pool, i.e. choices of model designs to select from, two 2 factors in this example data will lead to seven possibilities in the design pool:

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• If invoked from a Partek E/M method output, the data node contains raw read counts and the default normalization is:
  • Normalize to total count (RPM)
  • Add 0.0001 (offset)
• If invoked from a Cufflinks method output, the data node contains FPKM and the default normalization is:
  • Add 0.0001 (offset)

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Figure 6:  

If advanced normalization needs to be applied, perform the Normalize counts task on a quantification data node before doing differential expression detection (GSA or ANOVA).

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Click on Configure to customize Advanced options (Figure 7). Image Removed
Figure 7:

Low-expression feature

Low -expression feature section allows you to specify criteria to exclude features that do not meet requirements for the calculation.

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We recommend to use lognormal with shrinkage distribution (the default), an experienced user may want to click on Custom to configure the model type and p-value type (Figure 8). Image Removed
Figure 8:

If multiple distribution types are selected, then the number of total models that is evaluated for each feature is the product of number of design models and the number of distribution types. In the above example, suppose we have only compared A vs B in Cell type as in Figure 2, then the design model pool will have the following three models:

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