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Tutorial 5: Conclusion



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).


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