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Introduction
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A Tutorial on PCA
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A Tutorial on PCA
Contents
Introduction
Purpose
Prerequisites
CompClust Background
Principal Components Analysis (PCA) Background
Interpreting Principal Components
Data Preparation
Import the necessary software
Load a dataset
Construct a PCAGinzu object
Determining the Extreme Rows (aka Extreme Genes)
Setting the significant condition group threshold
Review PCA results using Tk graphics
View PCA projection scatter plots
View extreme point trajectory plots
Review PCA results using matplotlib graphics
View outlier scatter plot
View outlier trajectory plots
List a principal component's extreme points and significant conditions
Get a list of extreme points
View significant condition list
Analyzing condition covariates
Covariate analysis of the PGC diabetes dataset
Covariate analysis of Cho dataset
Performing batch PCA interpretation
Further Information
Publication: Mining Gene Expression Data by Interpreting Principal Components
The CompClust Python Package
Other CompClust Tutorials
Acknowledgements
Bibliography
Joe Roden 2005-12-13