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After normalizing the data, we can perform differential expression analysisanalysis to identify genes that are differentially expressed based on treatment.

• Select Click the Normalized counts data node
• Select Statistical Click Differential analysis from in the task menuSelect Detect differential expression (GSA) 
• from the Statistical analysis section of the task Click GSA in the task menu (Figure 1)
  
  

The Include Included attributes page shows all available attributes for analysis (Figure 2). Here, we only have one attribute, Treatment5-AZA Dose.

• Select Next to continue with Treatment with 5-AZA Dose as the selected attribute

The Comparison selector Comparisons page will open (Figure 3). 

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It is easiest to think about comparisons as the questions we are asking. In this case, we want to know what are the differentially expressed genes between untreated and treated cells. We can ask this for each dose individually and for both collectively. 

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• Select 5μM in the upper box
• Select 0μM in the lower box 
• Select Click Add comparison to add 5μM vs. 0μM to the comparison table (Figure 4)
  
  

• Repeat to create comparisons for 10μM vs. 0μM and 5μM:10μM vs. 0μM (Figure 5)
  
  

By default, the GSA will use a lognormal with shrinkage model for its analysis of variance of each gene. This is appropriate for most data sets and will tend to give accurate and reproducible results. We will use the default settings under Advanced Options in this tutorial. To learn more about the advanced options available in the GSA task, please see the Differential Gene Expression - GSA user guide. 

• Select Click Finish to perform GSA as configured

A Gene analysis GSA task node and a Feature list data node will be added to the pipeline (Figure 6). 

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