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. 

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

 

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.

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. 

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

 

PCA is particularly useful for identifying outliers and batch effects in data sets. We can see a batch effect in this dataset as samples separate by batch. To make this more clear, we can add an ellipses by Batch. 

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.