Principal Component Analysis (PCA)

Background

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a number of uncorrelated variables called principal components, related to the original variables by an orthogonal transformation. This transformation is defined in such a way that the first principal component has as high a variance as possible (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to the preceding components. PCA is sensitive to the relative scaling of the original variables.

In the field of microarray analysis, this method can be used to help identify the primary causes for differences in gene expression between samples.

Analysis

Simbiot microarray analysis implements PCA using native R functions.  The data may be analyzed directly or following clustering using k-means and self-organizing maps (SOM). 

Free demo accounts are available at http://www.simbiot.net.

Please also see more information about Simbiot Single User Accounts and Private Server installations as well as a brief introduction to microarray analysis.