In this tutorial we have taken a yeast cell cycle dataset with a strong cyclic behavior and examined it through Principal Component Analysis. During this survey we have considered three important elements of PCA: the variances in the data (Scree Plot), the relationship between the genes and the components (Loadings Line Plot and Loadings Color Matrix Plot), and the projection of the samples in the new components (Score Plot - Raw Data and Normalized). The Scree Plot indicated that the first two principal components captured most of the behavior of the data. The Loadings and Score Plots brought into relief the periodicity of the yeast cell cycle, both in genes and in time.
The analysis step in this tutorial are captured in the GeneLinker Script PCAGenes.gls. This is a single step script that applies PCA to any dataset. There is a similar script, PCASamples.gls that runs PCA with the opposite orientation.
GeneLinker's scripting capability is described in the GeneLinker script generation and script running documentation.
When you are finished, you can close all the open plots either by clicking on the 'x' box in the upper-right hand corner of each, or by selecting Close All from the Window menu.
1. Orly Alter, Patrick O. Brown & David Botstein, 'Singular value decomposition for genome-wide expression data processing and modeling', Proc. Nat. Acad. Sci. USA, 97, 10101-10106 (2000).
Where To Go From Here
Go through the other tutorials provided.
Read the Online Help to learn more about the various functions of GeneLinkerô.
Further explore GeneLinkerô by using additional features.
Load up your favorite data set and try out all the buttons and menu items.
Don't forget to right-click on things like plots - many details of graphics can be customized.
Visit the Predictive Patterns website at http://www.improvedoutcomes.com/ for the latest information on GeneLinkerô Gold enhancements and additional products.