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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

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Select () 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)

Numbered figure captions

SubtitleText	Configuring plot properties to color by treatment, shape by batch, size by time, and connect by treatment combination
AnchorName	Configure Plot Properties

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Numbered figure captions

SubtitleText	PCA scatter plot showing treatment, batch, and time information for each sample. A batch effect is clearly visible.
AnchorName	Configured PCA scatter plot

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

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Numbered figure captions

SubtitleText	Ellipses around batch groups show that samples separate by batch
AnchorName	PCA plot with ellipses

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

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