Jürgen von Frese
Title: Systematic Microarray Data Analysis: an overview
Recommended Reading
• Design and Analysis of DNA Microarray Investigations, Richard M. Simon, Edward L. Korn, Lisa M. McShane, Michael D. Radmacher, George W. Wright, Yingdong Zhao, Springer-Verlag, New York, 2003

A large number of useful technical reports from the same authors can be downloaded from: http://linus.nci.nih.gov/~brb/TechReport.htm

• Statistical Analysis of Gene Expression Microarray Data, Terry Speed, Chapman & Hall, 2003

• Analysis of microarray gene expression data, Wolfgang Huber, Anja von Heydebreck, Martin Vingron, in Handbook of Statistical Genetics, 2nd edition, Wiley (2003).
http://www.ebi.ac.uk/huber/docs/hvhv.pdf

• Computational Analysis of Microarray Data, John Quackenbush, Nature Reviews Genetics 2 (2001) 418-27

• The Bioinformatics of Microarray Gene Expression Profiling, John N. Weinstein et al., Cytometry 47 (2002) 46–49

• A good literature survey can be found at:
http://www.molgen.mpg.de/~heydebre/explit.html

Ursula Sauer
Title: Assessment of the quality of micro-array data
Recommended Reading

Ben Bolstad
Title: Data normalization and standardization
Recommended Reading
• Bolstad BM, Irizarry RA, Astrand M, and Speed TP. A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance.Bioinformatics, 19(2):185-193, 2003.

• Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, and Speed TP. Summaries of Affymetrix GeneChip Probe Level Data. Nucleic Acids Research, 31(4):e15, 2003.

• Yang YH, Dudoit, S, Luu P, Lin DM, Peng V, Ngai J, Speed, TP.
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation Nucl. Acids Res. 30: e15,2002

• Wu et al. (2004) A model-based background adjustment for oligonucleotide expression arrays., JASA 2004;99:909-917.

John D. Storey
Title: Multiple testing and False Discovery Rate
Recommended Reading

Yudi Pawitan
Title: testing, sample size, and sensitivity for micro-array studies
Recommended Reading
References
• Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A. False Discovery Rate and Sample Size for Microarray Studies. /Bioinformatics
/21: 3017-3024, 2005/./

• Jung SH (2005) Sample size for FDR-control in microarray data analysis.
Bioinformatics, 21, 3097-104.

Krina Zondervan
Title: SNPs, HAPMAP, genomic pattern of linkage disequilibrium
Recommended Reading
Refs:
• Hapmap paper -- Nature 2005
• Dawson et al Nature 2002
• Altshuler et al (blocks) Nature 2003
• Morley et al Nature 2003
• Cheung et al Nature 2005
• Phillips et al Nat Genet 2002
• Ke et al HMG 2005
• Haley paper, TIG 2004

Jaya Satagopan PhD
Title: Multi-stage analysis in SNP and gene expression based studies
Recommended Reading
Suggested References:
• JM Satagopan, DA Verbel, ES Venkatraman, KE Offit, CB Begg (2002). Two-stage designs for gene-disease association studies. Biometrics, 58: 163-170.

• JM Satagopan, RC Elston (2003). Optimal two-stage genotyping in population-based association studies. Genetic Epidemiology, 25: 149-157.

• JM Satagopan, ES Venkatraman, CB Begg (2004). Two-stage designs for gene-disease association studies with sample size constraints. Biometrics, 60: 589-597.

• EJ van den Oord, PF Sullivan (2003). A framework for controlling false discovery rates and minimizing the amount of genotyping in the search for disease mutations. Human Heredity, 56: 188-199.

• DC Thomas, RW Haile, D Duggan (2005). Recent developments in genomewide association scans: a workshop summary and review. American Journal of Human Genetics, 77: 337-345.

• DY Lin (2006). Evaluating statistical significance in two-stage genomewide association studies. American Journal of Human Genetics (in press). Early access of online publication available at:
http://www.journals.uchicago.edu/AJHG/journal/issues/v78n3/43151/43151.web.pdf

Richard S. Spielman, Ph.D.
Title: Locating polymorphic determinants of gene expression
Recommended Reading


David Edwards
Title: Selecting and Combining Markov Networks
Recommended Reading
• Friedman, N. (2004). Inferring Cellular Networks Using Probabilistic Graphical Models. Science, 303, 799-805.

• Wille, A. & Buhlmann, P. (2006). Low-order Conditional Independence Graphs for Inferring Genetic Networks.
Statistical Applications in Genetics and Molecular Biology, 5, 1, 1-31.

David M. Rocke
Title: Patient classification and prediction based on transcript profiling
Recommended Reading

Marina Vannucci
Title: Bayesian methods for simultaneous model-based clustering
of individuals and variable selection
Recommended Reading
• Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J.,Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon,N., Buckley, C. and Falciani, F. (2004). Bayesian variableselection in multinomial probit models to identify molecularsignatures of disease stage. Biometrics, 60(3), 812--819.

• Vannucci, M., Sha, N. and Brown, P.J. (2005).NIR and mass spectra classification: Bayesian methods forwavelet-based feature selection. Chemometrics and IntelligentLaboratory Systems, 77, 139--148.

• Tadesse, M.G., Sha, N. and Vannucci, M. (2005).Bayesian variable selection in clustering high-dimensional data.Journal of the American Statistical Association, 100, 602--617

• Tadesse, M.G., Vannucci, M. and Lio', P.(2004). Identification of DNA regulatory motifs using Bayesianvariable selection. Bioinformatics, 20(16), 2553--2561.

John Whittaker
Title: Integrated transcriptional profiling and QTL mapping for identification of genes underlying disease
Recommended Reading
A good introduction to the key ideas is:
• Jansen RC, Nap JP (2001) Trends Genet 17:388-391

Results for the experiment I will describe are in:
• Hubner N et al (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease Nature Genetics 37:243-253

Arief Gusnanto
Title: Analysis of micro-array data using mixture mixed model
Recommended Reading
References for background reading
• Main reference: Gusnanto, A., Ploner, A., Pawitan, Y. (2005). Fold-Change
Estimation and Identification of Differentially Expressed Genes using
Mixture Mixed-Model, Statistical Applications in Genetics and Molecular
Biology, 4(1), Article 26. http://www.bepress.com/sagmb/vol4/iss1/art26/

Supporting references:

• Efron, B., Tibshirani, R., Storey, J. D., Tusher, V. (2001). Empirical
Bayes analysis of a microarray experiment, Journal of the American
Statistical Association, 96(456), 1151-1160

• Kauermann, G, Eilers, P. (2004). Modeling microarray data using a threshold
mixture model, Biometrics, 60, 376-387

• Newton, M.A., Noueiry, A., Sarkar, D., Ahlquist, P. (2004). Detecting
differential gene expression with a semiparametric hierarchical mixture
method, Biostatistics, 5(2), 155-176

• Efron, B. (2004) Large-scale simultaneous hypothesis testing: the choice of
a null hypothesis, JASA, 99, 96-104.

Brian Tom
Title: Locating polymorphic determinants of cell function differentiation
Recommended Reading


Carlo Berzuini
Title: Conditional independence modelling of biological networks
Recommended Reading

Chris Holmes
Title: Bayesian Analysis of time –course gene expression data
Recommended Reading

Solveig Siebert
Title:Integrative genomics approaches for inferring causal associations
between gene expression and disease
Recommended Reading

Jennifer Taylor
Title:Deciphering the regulatory recipe for differentiating human epithelial cells
Recommended Reading

Erin Conlon
Title: Integrating DNA motif discovery and genome-wide expression analysis
Recommended Reading
References for methods:

• Conlon E.M., Liu X.S., Lieb J.D., Liu, J.S. (2003) Integrating regulatory motif discovery and genome-wide expression analysis. Proceedings of the National Academy of Sciences USA, 100, 3339-3344.

• Liu, X.S., Brutlag, D.L., Liu, J.S. (2002) An algorithm for finding protein DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol. 20, 835-839.

References for biological experiments:

• Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241-4257.

• Gasch, A. P., Huang, M., Metzner, S., Botstein, D., Elledge, S. J.&Brown, P. O. (2001) Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p. Mol. Biol. Cell 12, 2987–3003.

• Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen,
M.B., Brown, P.O., Botstein, D., Futcher, B. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273-3297.

Ernst Wit
Title: Design of microarray based studies
Recommended Reading
• Wit, E, Nobile, A, Khanin, R, (2005) "Near-optimal designs for dual-channel microarray studies," Applied Statistics, 54(5):817-30.

• Byrne KA, et al. (2005) "Gene expression profiling of muscle tissue in Brahman steers during nutritional restriction" JAS, 83:1-12