In a principal component analysis (PCA), the total number of variables in the dataset is reduced to a smaller number of variables – the principle components of the dataset.
Principal component variables are calculated from correlated variables in the total dataset. In other words, the principal component analysis is a workflow used to identify variance in a dataset. The analysis can be run on an entire microarray chip, or on a pathway.
To begin the analysis, see Running the Analyses, then perform the following steps.
Only one subset may be specified in this analysis. Information in Subset 2 will be ignored.
To perform a PCA analysis:
Click the Advanced Workflow tab, then open the Analysis menu.
The Variable Selection section appears.
Drag a high-dimensional data node () into the Variable Selection box.
Click the High Dimensional Data button.
The Compare Subsets-Pathway Selection dialog appears.
Specify the platform and other filters for the analysis.
For information, see High Dimensional Data.
Click Apply Selections.
Optionally, select either or both of the following:
Click Run. Your analysis appears below:
For more information regarding PCAs, see: http://psb.stanford.edu/psb-online/proceedings/psb00/raychaudhuri.pdf.