How to do a principal component analysis (PCA)?
Principal component analysis in one of the multivariate techniques for summarizing a large data matrix to a few manageable dimensions. It is also useful for visualizing similarities in expression profiles among loci. To perform a PCA, click Analysis|Principal component analysis. A dialog box appears for you to specify options. Again, if you are using the yeast200.xls file, uncheck the Has missing value check box because there is no missing value in the data set. Choose whether you wish to run PCA on a correlation or a variance-covariance matrix, how many principal components to have, whether to have graphic output (i.e., plots of principal component scores), and whether to standardize the principal component score or not. Click the OK button and AMADA will perform PCA in a few seconds, outputting the correlation or variance-covariance matrix, eigenvalues, eigenvectors and principal component scores. Once PCA is finished, you may choose to plot the first few principal component scores t
