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If there is more than one factor in the model, more complex criteria combining the factors can be specified using the gene list creator. The Create Gene List tool is available under the Analysis section of the workflow. For example, to find categories that are significant and changed by at least two fold, make two criteria, one for a low p-value and the other for a minimum of two fold change, and take the intersection of the two. This is the only way to analyze GO ANOVA if no factor is interacted with genes.

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If looking for disruption for groups which are not so much differentially expressed, but instead which express different genes for different treatments, filter for low disruption p-values but for high factor p-values. As shown by Figure 2, large or diverse groups that are differentially expressed will often exhibit significant disruption. In fact, a group that is differentially expressed but includes even a single gene that is not changed will have very significant disruption. These situations are certainly notable, but are distracting if looking for functional groups that instead are uniquely patterned based on treatment. By filtering out those groups with low p-values for the factor and then looking at the remaining groups with low p-values for disruption, groups observed have usually very distinct patterns of expression (Figure 3).