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

• Click the Normalized counts node
• Click Statistics in the task menu
• Click Differential analysis in the task menu (Figure 1)
  
  

Select the appropriate differential analysis method (Figure 2). In this tutorial we are going to use GSA, but Partek Flow offers a number of alternatives. Hover the mouse over the [...] symbol for more informations.

The Included attributes page shows all available attributes for analysis (Figure 3). Here, we only have one attribute, 5-AZA Dose.

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

The Comparisons page will open (Figure 4). 

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. 

The upper box will be the numerator and the lower box will be the denominator in the comparison calculation so we will select the 0μM control in the lower box. 

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

• Repeat to create comparisons for 10μM vs. 0μM and 5μM:10μM vs. 0μM (Figure 6)
  
  

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. To learn more about the advanced options available in the GSA task, please see the GSA user guide. 

• Click Finish to perform GSA as configured

A GSA task node and a GSA data node will be added to the pipeline (Figure 7).