...

Principal Components Analysis (PCA) is an excellent method to visualize similarities and differences between the samples in a data set. PCA can be invoked through a workflow, by selecting () from the main command bar, or by selecting Scatter Plot from the View section of the main toolbar. We will use a workflow. 

• Select Gene Expression from Select Gene Expression from the Workflows drop-down menu
• Select PCA Scatter Plot from the QA/QC section of the Gene Expression workflow

...

We can change the plot properties to better visualize the effects of different variables. 

• Select (Image Added) to open the ConfigurePlot Properties dialog
• Set Shape to 4. Batch
• Set Size to 3. Time
• Set Connect to 5. Treatment Combination
• Select OK (Figure 2)
  
  

The PCA scatter plot now shows information about treament, batch, and time for each sample (Figure 3).

• Select (Image Added) to open the ConfigurePlot Properties dialog
• Select Ellipsoids from the tab
• Select Add Ellipse/Ellipsoid
• Select Ellipse
• Select Batch from the Categorical Vairable(s) panel and move it to the Group Variable(s) panel
• Select OK
• Select OK to close the dialog

The ellipses help illustrate that the data is spearated by batches (Figure 4). 

Ways to address the batch effect in the data set will be detailed later in this tutorial.