This tutorial introduces you to Principal Component Analysis (PCA). You will be shown how to perform the PCA experiment and then visualize the results in different types of plots.
Skills You Will Learn:
How to import gene expression data from a file into the GeneLinkerô database.
How to perform a PCA experiment.
How to visualize the results of a PCA experiment in various plots.
How to use the 3D plot functions.
Principal Component Analysis
A number of recently published analyses of gene expression data have centered their attention on Principal Component Analysis (PCA) as a method of extracting more information from data. We will study this application using the yeast elutriation dataset studied by Alter, Brown & Botstein [Alter2000].
The traditional application of PCA is to reduce the dimensionality of data. In gene expression experiments, where there are typically thousands of variables, it can be extremely useful to collapse the genes into a smaller set of principal components. This makes most types of plots easier to interpret, which can help to identify structure in the data.
In Alter et al, they discuss a dataset that explores the gene expression over time in yeast during an elutriation study. They include 14 measurements at half-hour intervals. One of the goals of the study was to verify whether there were cyclic patterns in gene expression that were commensurate with the yeast cell cycle. A related question was whether the genes known to be involved in various stages of the cell cycle would show time-shifted expression waves.
This tutorial should take about 30 minutes, depending on how long you spend investigating the data, and how fast your machine is.
If you must stop part way through the tutorial, simply exit the program by selecting Exit from the File menu. The data and experiments you have performed to that point are saved automatically by GeneLinkerô. The next time you start GeneLinkerô, you can continue on with the next step in the tutorial.