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The method used to detect changes in functional groups is ANOVA. For detailed information about ANOVA, see Chapter 11 of the Partek User Manual. There is one result per functional group based on the expression of all the genes contained in the group. Besides all the factors specified in the ANOVA model, the following extra terms will be added to the model by Partek Genomics Suite automatically:

• Sample ID - Since multiple measurements (on the multiple genes within the functional group) come from the same sample, sample ID is added to the model, otherwise the ANOVA assumption of sample independence is violated
• Gene ID - Since not all genes in a functional group express at the same level, gene ID is added to the model to account for gene-to-gene differences
• Factor * Gene ID (optional) - Interaction of gene ID with the factor can be added to detect changes within the expression of a GO category with respect to different levels of the factor, referred to in this document as the disruption of the categories expression pattern or simply disruption

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y = µ + T + P + G + T *G + S(T * P) + ε

• y: expression of a functional group
• µ: average expression of the functional group
• T: tissue-to-tissue effect
• P: patient-to-patient effect (this is can be specified as a random effect)
• G: gene-to-gene effect (differential expression of genes within the function group independent of tissue type)
• T*G: Tissue-Gene interaction (differential patterning of gene expression in different tissue types)
• S (T*P): sample-to-sample effect (this is a random effect, and nested in tissue and patient)
• ε: error term

In the case that there is more than one data column mapping to the same gene symbol, Partek Genomics Suite will assume that the markers target different isoforms and will not treat the two markers as replicated of the same gene. Instead, each column is treated as a gene unto itself.

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