Genomics Suite Documentation

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  • Experimental factors
  • Factors explaining sample dependence
  • Factors explaining noise

For more details on ANOVA, see Chapter 11 of the User’s Manual.

 

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SubtitleTextGO ANOVA setup dialog. Including a factor in the ANOVA model (ANOVA Factors) will identify gene ontology (GO) categories whose expression is different across the genes within the category, by the factor of interest. Including a factor as a Disruption Factor will identify GO categories where the expression of the genes within the category are affected but not uniformly across the genes withing the category. Genes (probesets) can be excluded based on expression levels, to reduce the noise.
AnchorNameSetting up GO ANOVA

 

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Sometimes factors do not act independently of each other. For example, different dosages of a drug may affect patients differently over time, or a drug may not affect tissues equally as in many toxicity studies. If the effect of one variable on the other is either suspected of occurring, or of particular interest, an interaction between the two factors should be included. To do this, select the two factors simultaneously by CTRL-clicking the factors and then select Add Interaction.

Factors Explaining Sample Dependence

Factors to control for sample dependence include variables that account for relation between samples. If tissues are collected in pairs from the same patient, patient ID would be included. Similarly if tissues are collected from two distinct populations, this variable should probably be included as well.

Factors Explaining "Noise"

Noise variables may be caused by technical processes used during sample collection and processing. Scan data and dye color are often among these variables.

Optional Disruption Factor(s)

 

 

 

 

Additional assistance

 

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