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