# Exploring the data set with PCA
The principal components analysis (PCA) scatter plot allows us to visualize similarities and differences between the samples in a data set.
* Click the **Normalized counts** data node
* Click **Exploratory analysis** in the task menu
* Click **PCA**
* Click **Finish** to run PCA with the default options
A PCA task node and a PCA data node will be added to the pipeline (Figure 1)
Figure 1. PCA task node and data node are added to the pipeline
* Double click the **PCA** data node to open the PCA scatter plot and corresponding information in the Data Viewer (Figure 2)
Figure 2. Viewing the PCA plot in Data Viewer
In the Data Viewer, select the 3D PCA scatter plot then click **Style** under Configure and set the Color by drop-down to **5-AZA Dose**. The scatter plot shows each sample as a sphere, colored by treatment group, in a three dimensional plot. The x, y, and z axes are the first three principal components. The percentage of total variance explained by each is listed next to the axis label. The size of each axis is determined by the variance along that axis. The plot is fully interactive; it can be rotated and points selected.
Here, we can see that samples separate based on treatment, but there is noticeable separation within treatment groups, particularly the 0μM and 10μM treatment groups.
For more detailed information about the PCA scatter plot, please see the [PCA](/partek-flow/user-manual/task-menu/exploratory-analysis/pca.md) user guide.
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