Slides and articles related to microrarray technology:

• Duke template for Affymetrix files gives a brief description of Affymetrix data files
• Magic of Microarrays, a recent Scientific American article overview
• Microarray slides (powerpoint)
• Some useful web sites
• Affyx data processing - basic details
• Affymetrix manual more Affymetric details
• GenePix - brief description of spotted array processing
• More links, slides, articles made available in class.

Molecular biology reference texts:

• Molecular Cell Biology, 4th ed (Darnell), Freeman; particularly Part I and Part II. Probably the best textbook source for in-depth information.
• Recombinant DNA, 2nd ed (Watson), Freeman; more condensed and focused and a bit more on molecular biology techniques.

Statistics reference texts: (all on reserve in Vesic Library)

• The STA 270/BGT 200 support text and tutorial/web material on statistical analysis using R at the STA 270/BGT 200 web site are particularly useful (and in any case prerequisite) on general statistical modelling and methods (regressions, likelihood and Bayesian inference, ranges of statistical computational methods, etc).

• An excellent text on linear models is Applied Linear Regression, 2nd Edition by S. Weisberg, 1985, John Wiley & Sons. This is the course text for STA 244.
• Useful material on binary regression models in Chapter 3 of Ordinal Data Modeling by V.E. Johnson and J.H. Albert, 1999, Springer Verlag. Bayesian Data Analysis by A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Chapman & Hall. It covers regression and binary regression among many other topics including key aspects of applied Bayesian methods.

• Applied Multivariate Statistical Analysis by R.A. Johnson and D.W. Wichern (Prentice Hall, 1992) is a comprehensive but very readable book on multivariate statistical analysis. It contains very useful material on matrix analysis, singular value and principal components decompositions, and clustering methods, all that are relevant to this class.
• Pattern Recognition and Neural Networks by B.D. Ripley (1996, Cambridge University Press) covers a wide range of relevant topics
• A generally useful, more advanced Bayesian statistics methods book is
• The Elements of Statistical Learning by T. Hastie, R. Tibshirani & J. Freidman, Spring Verlag, 2001, covering a broad range of modern methods for regression, prediction and statistical data mining

Papers and manuscripts on gene expression analysis, profiling and statistical modelling:

• Many papers provided in class.