References for Microarray Data Analysis

PubH 5470-2 (Spring 2002)

(With comments in parentheses)

http://www.biostat.umn.edu/~weip/course/ge/ref02s.html

Introduction to microarray technologies
1. Brown P and Botstein D. Exploring the new world of the genome with DNA microarrays. Nature Genetics Supplement 21:33-37, 1999.
  (A general introduction on cDNA array technology and its applications)
2. Duggan DJ, Bittner M, Chen Y, Meltzer P and Trent JM. Expression profiling using cDNA microarrays. Nature Genetics Supplement 21:10-14, 1999.
  (Intro to cDNA arrays)
3. Lipshutz RJ, Fodor SPA, Gingeras TR and Lockhart DJ. High density synthetic oligonucleotide arrays. Nature Genetics Supplement 21:20-24, 1999.
  (Intro to Affy oligonucleotide arrays)
4. Affymetrix Inc. Statistical Algorithms Reference Guide.
  (Intro to algorithms being used to summarize gene expression levels for Affymetrix Microarray Suite version 5.0)
5. Li C and Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. PNAS, 98:31-36, 2001.
  (Use of a multiplicative model to summarize expression levels for Affy arrays)
6. Li C and Wong WH. Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biology, 2001/2/8/research/0032.
  (Further development and more numerical evaluations)
Detecting differentially expressed genes
1. Chen Y, Dougherty ER and Bittner ML. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomedical Optics, 2:364-367, 1997.
  (Probably the earliest paper on statistical analysis of array data; use of the Wilcoxon nonparametric test; proposed Normal-based parametric models)
2. Newton M etc. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology, 8:37-52, 2001. (Online access at Uof M)
  (Parametric Bayesian approach, w/o replications)
3. Lin Y, Nadler ST, Attie AD and Yandell BS. Mining for low-abundance transcripts in microarray data. PS
  (Nonparametric approach, w/o replications)
4. Kerr MK, Martin M and Churchill GA. Analysis of variance for gene expression microarray data. Journal of Computational Biology, 7:819-837, 2000. (Online access at Uof M) or PDF
  (Use of ANOVA model)
5. Kerr, M.K. et al. Statistical analysis of a gene expression microarray experiemnt with replication. To appear Statistica Sinica, 2002. PDF
6. Dudoit S, Yang YH, Callow MJ and Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiements. Statistica Sinica, 12:111-139, 2002. PS
  (Use of loess curve to center; permutation test using t-statistic; adjustment for multiple tests)
7. Ideker, T., Thorsson, V., Siehel, A.F. and Hood, L.E. (2000). Testing for differentially-expressed genes by maximum likelihood analysis of microarray data. Journal of Computational Biology, 7, 805-817. access at U of M)
  (A Normal-based linear regression approach)
8. Thomas, J.G., Olson, J.M., Tapscott, S.J. and Zhao, L.P. (2001). An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. {\em Genome Research}, {\bf 11}, 1227-1236.
  (A regression approach using the robust/sandwich estimator)
9. Wolfinger RD, et al. Assessing gene significance from cDNA microarray expression data via mixed models. PDF J of Computational Biology, 8:625-637. access at Uof M)
  (Use of Normal-based linear mixed models to do normalization and detecting differential expression)
10. Tusher VG, Tibshirani R and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. PNAS, 98:5116-5121, 2001.
  (Two conditions with replications; SAM: use permutation-type tests and FDR to control for multiplicity)
11. Efron B, Tibshirani R, Goss V and Chu G. Microarrays and their use in a comparative experiment. 2000. PS (A modified/newer version: Efron B, Tibshirani R, Storey JD, and Tusher V. (2001) Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96:1151-1160, 2001)
  (Empirical Bayesian and Frequentist approaches, with replications)
12. Pan W. A Comparative Review of Statistical Methods for Discovering Differentially Expressed Genes in Replicated Microarray Experiments. To appear in Bioinformatics. Also Research Report 2001-028, Division of Biostatistics, University of Minnesota, 2001. PS or pdf
  (Compared the t-test, the Wilcoxon rank test, the robust regression of Thomas et al, the EB of Efron et al, the SAM of Tusher et al, and the mixture model of Pan et al)
13. Pavlidis P and Noble WS. Analysis of strain and region variation in gene expression in mouse brain. Genome Biology, 2001/2/10/research/0042.
  (Normal-based two-way ANOVA for two factors with possibly more than two categories)
14. Baldi P and Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17: 509-519, 2001. access at U of M) (Parametric Bayesian approach to t-test)
Clustering: hierachical, K-means, SOM and model-based clustering.
1. Eisen M, Spellman P, Brown P and Botstein D. Cluster analysis and display of genome-wide expression patterns. PNAS, 95:14863-14868, 1998.
  (hierachical clustering)
2. Tavazoie et al. Systematic determination of genetic network architecture. Nature Genetics, 22:281-285, 1999.
  (K-means clustering)
3. Tamayo et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differntiation. PNAS, 96:2907-2912, 1999.
  (SOM clustering)
4. Zhang K and Zhao H. Assessing reliability of gene clusters from gene expression data. Funct Integr Genomics, 1:156-173, 2000.
5. Kerr MK and Churchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. PNAS, 98:8961-8965, 2001. PDF
6. Tibshirani R, Walther G, Botstein D and Brown P. Cluster validation by prediction strength. PS
7. Lee M-L T, Kuo FC, Whitmore GA and Sklar J. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. PNAS, 97:9834-9839, 2000.
  (One condition with replications: A mixture of two normals)
8. Pan W, Lin J and Le C. Model-based cluster analysis of microarray gene expression data. Genome Biology, 3(2): research0009.1-0009.8, 2002.
  (Model-based clustering of t-statistics to explore differential gene expression)
9. Ghosh D and Chinnaiyan AM. Mixture modelling of gene expression data from microarray experiments. Bioinformatics, 18:275-286, 2002. access at U of M)
  (Model-based clustering of gene expression patterns)
10. Bhattacharjee et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. PNAS, 98:13790-13795, 2001.
11. Zhang K and Zhao H. Assessing reliability of gene clusters from gene expression data. Funct Integr Genomics, 1:156-173, 2000.
12. Hastie T et al. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology, 1(2):research0003.1-0003.21, 2000.
13. Hastie T et al. Supervised harvesting of expression trees. Genome Biology, 2(1):research0003.1-0003.12, 2001.
14. Li H and Hong F. Cluster-Rasch models for microarray gene expression data. Genome Biology, 2(8)}:research0031.1-0031.13, 2001.
Classification: discriminant analysis
1. Golub T et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531-537, 1999.
  (Proposed a weighted voting algorithm)
2. Slonim DK, Tamayo P, Mesirov JP, Golub TR and Lander ES. Class prediction and discovery using gene expression data.
  (More detailed description on the methods of Golub et al.)
3. S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. June 2000. PS
4. Radmacher MD, McShane LM and Simon R. A paradigm for class prediction using gene expression profiles. Technical Report 001, National Cancer Institute.
  (Assessing the "significance" of classoification results using a breast cancer data set)
5. Hedenfalk I et al. Gene-expression profiles in hereditary breast cancer. New England Journal of Medicine, 344:539-548, 2001. PDF
  (More scientific background and results using the previous breast cancer data set)