Analysis of Microarrays

Dr. Rustam Yukhananov and Dr. Alexander Loguinov, Brigham and Women's Hospital, Harvard Medical School

THEORY 1: The Microarray: basic technology

• Microarray fabrication (clone selection, probe preparation)
• Different technologies for microarray analysis
• Image analysis (gridding, segmentation, intensity extraction)

THEORY 2: Microarray applications and data analysis

• Experimental design for typical application
• Statistical methods of data analysis (data preprocessing, robust regression analysis, ANOVA

LABORATORY 1 (joint with A. Loguinov)

• Practical aspects of image analysis (gridding, segmentation, intensity extraction)
• Data processing within R environment
• Quality control issues:
  • how it should look for an "ideal" case (using Monte Carlo simulations)
  • could we measure real concentration of molecules with microarray (optional)

LABORATORY 2

• Exploratory and confirmatory differential gene expression analysis for different microarray platforms based on robust statistical inference in parallel for real and Monte Carlo simulated data
• Variance Component Analysis (Balanced or Unbalanced Mixed Models) to quantify sources of variability in microarray experiment
• Supervised (Linear Discriminant Analysis and SVM) and Unsupervised Clustering (Agglomerative Hierarchical and Model-based Methods, K-means, SOM, and PCA).