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

The PCA scatter plot will open as a new tab (Figure 1).

Figure 1. Viewing the PCA scatter plot. Each point is a sample. Samples are colored by treatment.

In this PCA scatter plot, each point represents a sample in the spreadsheet. Points that are close together in the plot are more similar, while points that are far apart in the plot are more dissimilar. 

To better view the data, we can rotate the plot.

• Select () to activate Rotate Mode
• Click and drag to rotate the plot 

Rotating the plot allows us to look for outliers in the data on each of the three principal components (PC1-3). The percentage of the total variation explained by each PC is listed by its axis label. The chart label shows the sum percentage of the total variation explained by the displayed PCs. 

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

PCA is particularly useful for identifying outliers and batch effects in data sets. 

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