Links

Software

• The R Project for Statistical Computing. R is `GNU S' - A language and environment for statistical computing and graphics.
• The Comprehensive R Archive Network: CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for the R statistical package.
• Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data.
• Bioinformatics and Computational Biology Solutions using R and Bioconductor, edited by R.Gentleman, V.Carey, W.Huber, R.Irizarry, S.Dudoit, Springer 2005.
  This book covers most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms. See the companion website for R code and packages.
• Some packages hosted at the Computational Diagnostics Group.

Literature

Overviews and introductions

• Please read the Introduction to R as preparation for the course.
• Overview of Literature on Microarray Gene Expression Data Analysis by Anja von Heydebreck.
• Nature Genetics Supplements: Chipping Forecast I (Jan 1999), II (Dec. 2002), and III (June 2005)
• Analysis of microarray gene expression data (Huber, v.Heydebreck, Vingron, 2002)
• Bioconductor: Open software development for computational biology and bioinformatics (Gentleman et al., 2004)
• Statistical Challenges in Functional Genomics (Sebastiani et al., 2003, Statistical Science, 18:33-70)
• Introductory Statistics with R (Dalgaard, 2004, ISBN: 0387954759)

Research papers covered in the lectures

• Variance stabilization applied to microarray data calibration and to the quantification of differential expression (Huber et al., 2002)
• Differential Expression with the Bioconductor Project (v. Heydebreck, Huber, Gentleman, 2004)
  Selecting differentially expressed genes from microarray experiments (Pepe et al., 2003)
  Multiple Hypothesis Testing in Microarray Experiments (Dudoit et al., 2002)
• Diagnosis of multiple cancer types by shrunken centroids of gene expression (Tibshirani et al., 2002)
  Class Prediction by nearest shrunken centroids, with applications to DNA microarrays (Tibshirani et al., 2003)
• Selection bias in gene extraction on the basis of microarray gene-expression data (Ambroise and McLachlan, 2002)
  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification (Simon et al., 2003)