General:

• See the Computing link for books, manuals tutorials etc related to computing
• Papers on expression profiling in breast cancer are available from our [CB]² Bayesian Bioinformatics Group
• Slides from a recent talk on the Duke breast cancer microarray study -- in postscript and pdf formats
• A short and very useful list of key and recent references to DNA microarray work, plus some web links to other useful sites, at the Harvard statistics/biostatistics web site of Wing Wong and Jun Liu

Some specific papers and sites:

• Iyer et al, Science, Jan 99 (pdf format)
• Iyer et al, Nature, Jan 01 (pdf format)
• Zhao et al, Genes & Development, 00 (pdf format)
• Papers by Rob Tibshirani on gene shaving, clustering etc., including a useful 1999 review/overview of clustering methods
• A catalogue of interesting papers from the Expression statistics seminar (fall 2000) at the German Cancer Research Center (Heidelberg)
• IPAM Fall 2000 Functional Genomics Program, with links to tutorials and talks from a couple of relevant conferences, as well as research groups and literature
• Terry Speed's group at Berkeley (statistical modelling and analysis of cDNA microarray data)
• Many useful links at the DNA Microarray (Genome Chip) Web site
• General array information and commercial links
• Introductory articles and other links

Statistics reference texts: (all on reserve in Perkins)

• Almost any modern regression analysis text book - graduate level - will provide suitable coverage of linear regression. One excellent text 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.
• 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.
• For students more interested in the classification area, the excellent book Pattern Recognition and Neural Networks by B.D. Ripley (1996, Cambridge University Press) covers a wide range of topics at a very readable level, even for non-specialists. It also has a wealth of general background material.
• A generally useful, more advanced Bayesian statistics methods book is 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.