Difference between revisions of "PCA"
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Revision as of 17:40, 26 November 2013
Principle Component Analysis (PCA)
Principle component analysis is used to find the most important contributors to the variance in a dataset.
geWorkbench can dispatch a PCA job to a GenePattern server, and display the returned result.
The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.
