The eigenvectors are orthogonal. The first principal component is the eigenvector with the largest eigenvalue, and specifies the direction with the largest variability in the data. The second principal component is the eigenvector with the second largest eigenvalue, and specifies the direction with the second largest variability. Similarly for the third, etc. The data can be projected onto the space spanned by the eigenvectors. A plot of the data in the space spanned by the first and second principal component will show a simplified version of the data with variability in other directions than the two major directions of variability ignored.
To start the analysis:
Toolbox | Microarray and Small RNA Analysis ()| Quality Control | Principal Component Analysis ()
Select a number of samples ( () or ()) or an experiment () and click Next.
This will display a dialog as shown in figure 30.64.
In this dialog, you select the values to be used for the principal component analysis (see Selecting transformed and normalized values for analysis).
Click Finish to start the tool.