Principal components analysis (PCA) is a way to compare samples. Through dimensionality reduction algorithms, sample information is condensed to five principal components, which represent a combination of all the variables into one score. The scores can then be plotted—clusters on the plot represent a group of samples that have similar scores, and therefore similar overall results.
Set up your analysis
While viewing your experiment, click "+ Analysis" and select Principal components.
Select whether or not you would like to include all of your samples in your analysis
If you would only like to analyze certain groups within your sample data, select the groups you would like to include
Choose whether or not to shift data so zero is at the center (recommended)
Choose whether or not to scale data so it has a unit variance (recommended)
Click the "Run Analysis" button to begin running principal components analysis with the parameters you set above.
Customize your plots
Use the Plot tab to customize the title, color palette, and legend.
Choose which principal components to analyze out of 5 options (PC1, PC2, PC3, PC4, or PC5 on x axis, same options for y axis).
Select which variables you'd like to use to group your points