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Microarray Data Analysis

References

Other Bibliographies

• Wentian Li's References. Organized by year. Rockefeller University
• Brian Yandell's References. Organized by topic. University of Wisconsin.
• Wei Pan's References. Organized by topic. University of Minnesota.
• Leung Yuk Fai's References. Includes a weekly watch for microarray articles in medical journals.
• Giovanni Parmigiani's Reading List for Statistics for Gene Expression. Two or three key references under each topic. Johns Hopkins University.
• Medline Workbench. Includes a monthly list of “What's New” in microarrays.

Microarray Technology

• Duggan, D. J., Bittner, M., Chen, Y., Meltzer, P., Trent, J. M. (1999). Expression profiling using cDNA microarrays. Science 283(5398), 83-87. 1/1999.

Reviews and Collections

• Tumor Analysis Best Practices Working Group (2004). Expression profiling - best practices for data generation and interpretation in clinical trials. Nature Reviews Genetics 5, 229-238. 3/2004
• Tilstone, C. (2003). Vital statistics. DNA microarrays have given geneticists and molecular biologists access to more data than ever before. But do these researchers have the statistical know-how to cope? Nature 424, 610-612. (PDF) 8/2003
• Causton, H. C., Quackenbush, J., and Brazma, A. (2003). Microarray Gene Expression Data Analysis: A Beginner's Guide. Blackwell Publishing, Malden. 5/2003.
• Speed, T. P. (ed.) (2003). Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC, Boca Raton. 4/2003
• Parmigiani, G., Garrett, E. S., Irizarry, R. A., and S. L. Zeger, S. L. (eds.) (2003). The Analysis of Gene Expression Data: Methods and Software. Springer, New York. 3/2003 (www page)
• Dudoit, S., and Yang, Y. H. (2003). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software, Springer, New York. pp. 73-101. 3/2003
• Smyth, G. K., Yang, Y.-H., Speed, T. P. (2003). Statistical issues in microarray data analysis. In: Functional Genomics: Methods and Protocols, M. J. Brownstein and A. B. Khodursky (eds.), Methods in Molecular Biology Volume 224, Humana Press, Totowa, NJ, pages 111-136. (PDF) 3/2003
• Lorkowski, S., and Cullen, P. (2004). Analysing Gene Expression, A Handbook of Methods: Possibilities and Pitfalls. Wiley. 3/2003.
• Slonim, D. K. (2003). From patterns to pathways: gene expression data analysis comes of age. Nature Genetics supplement 32, 502-508.
• Nature Genetics Editor (eds.) (2003). Chipping Forecast II. Nature Genetics Supplement 32, 461-552. 12/2002
• Nguyen, D. V., Arpat, A. B., Wang, N., and Carroll, R. J. (2002). DNA microarray experiments: biological and technological aspects. Biometrics 58, 701-717. 12/2002
• Yang, Y. H., and Speed, T. P. (2002). Introduction to microarray bioinformatics, Parts 1-4. In: DNA Microarrays: a Molecular Cloning Manual, D. Bowtell and J. Sambrook (eds.), Cold Spring Harbor Press, New York.
• Baldi, P. and Hatfield, G. W. (2002). DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling. Cambridge University Press, Cambridge. 9/2002
• Shoemaker, D. D., and Linsley, P. S. (2002). Recent developments in DNA microarrays. Current Opinion in Microbiology 5, 334-337. 6/2002
• Firestein, G. S., and Pisetsky, D. S. (2002). DNA Microarrays: Boundless Technology or Bound by Technology? Guidelines for Studies Using Microarray. Arthritis and Rheumatism 46, 859–886. (PDF) 4/2002
• Dudoit, S., Yang, Y. H., and Bolstad, B. (2002). Using R for the analysis of DNA microarray data. R News 2 (1), 24-32. (Rnews website)
• Gibson, G. (2002). Microarrays in ecology and evolution: a preview. Molecular Ecology 11, 17-24.
• Bittner, M. L., Chen, Y., Dorsel, A. N., and Dougherty, E. R. (2001). Microarrays: Optimal Technologies and Informatics. Proceedings of SPIE Vol. 4266.
  [Papers on both cDNA and oligonucleotide arrays under the headings 1 Image Data Analysis, 2 Detecting Signals, 3 Data Normalization and Quality Control and 4 Analysis of Multiple Expression Profiles.]
• Quackenbush, J. (2001). Computational analysis of microarray data. Nature Review Genetics 2, 418-427. 6/2001
  [Review of data analysis techniques with emphasis on clustering.]

Microarray Databases

• Microarray standards at last. Nature 419, 323.
• A Brazma, P Hingamp, J Quackenbush, G Sherlock, P Spellman, C Stoeckert, J Aach, W Ansorge, C A Ball, H C Causton, T Gaasterland, P Glenisson, F C P Holstege, I F Kim, V Markowitz, J C Matese, H Parkinson, A Robinson, U Sarkans, S Schulze-Kremer, J Stewart, R Taylor, J Vilo and M Vingron (2001). Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nature Genetics 29, 365-371. 12/2001

Experimental design

• Yang, Y. H., and Speed, T. P. (2003). Design and analysis of comparative microarray experiments. In T. P. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC Press, pages 35-91. 4/2003
  [Includes a discussion of factorial and linear models.]
• Churchill, G. (2002). Fundamentals of experimental design for cDNA microarrays. Nature Genetics (Supplement) 32, 490-495. 12/2002
• Glonek, G. F. V., and Solomon, P. J. (2002). Factorial and time course designs for cDNA microarray experiments. Technical Report, Department of Applied Mathematics, University of Adelaide. 10/2002
  [The first careful treatment of factorial microarray experiments. Shows saturated or "all comparisons" designs are not generally efficient for estimating interactions.]
• Yang, Y. H., and Speed, T. P. (2002). Direct and indirect hybridizations for cDNA microarray experiments. Sankhya Series A 64, 707-721. 10/2002
  [A careful comparison of the efficiency of a direct comparison A vs B experiment relative to the same experiment using a reference, A vs R and B vs R. Allows for a detailed and realistic covariance structure.]
• Yang, Y. H., and Speed, T. P. (2002). Design issues for cDNA microarray experiments. Nature Reviews Genetics 3, 579-588. 8/2002
• Pan, W., Lin, J. and Le, C. (2002). How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biology 3(5): research0022.1-0022.10. 4/2002
• Churchill, G. A., and Oliver, B. (2001). Sex, flies and microarrays. Nature Genetics 29, 355-356. 12/2001
  [Commentary on Jin et al (2002), Nature Genetics 29, 389 - 395.]
• Kerr, M. K., and Churchill, G. A. (2001). Experimental design for gene expression microarrays. Biostatistics 2, 183-201. (PDF) 6/2001
  [Applies classical statistical experimental design to cDNA microarray experiments. A single-channel approach: keeps cy3 and cy5 spot intensities separate in the analysis. Normalization issues are not dealt with.]
• Craig, B. A., Vitek, O., Black, M. A., Tanurdzic, M., Doerge, R. W. (2001). Designing microarrays. In Proceedings of the 13th Annual Kansas State University Conference on Applied Statistics in Agriculture, Kansas State University, pages 159-182. 4/2001
• Black, M. A., and Doerge, R. W. (2001). Calculation of the minimum number of replicate spots required for detection of significant gene expression fold changes for cDNA microarrays. In Proceedings of the 13th Annual Kansas State University Conference on Applied Statistics in Agriculture, Kansas State University, pages 144-158. 4/2001
• Kerr, M. K., and Churchill, G. A. (2001). Statistical design and the analysis of gene expression microarrays. Genetical Research 77, 123-128. (PDF)
  [Applies classical statistical experimental design to cDNA microarray experiments. Keeps cy3 and cy5 spot intensities separate in the analysis. Normalization issues are not dealt with.] 

Pooling vs Non-Pooling

• Han, E.-S., Wu, Y., Bolstad, B., and Speed, T. P. (2003). A study of the effects of pooling on gene expression estimates using high density oligonucleotide array data. Department of Biological Science, University of Tulsa, February 2003.
• Kendziorski, C.M., Y. Zhang, H. Lan, and A.D. Attie. (2003). The efficiency of mRNA pooling in microarray experiments. Biostatistics 4, 465-477. 7/2003
• Xuejun Peng, Constance L Wood, Eric M Blalock, Kuey Chu Chen, Philip W Landfield, Arnold J Stromberg (2003). Statistical implications of pooling RNA samples for microarray experiments. BMC Bioinformatics 4:26. 6/2003

Image analysis

See also groups which  produce image analysis software.

• Angulo, J., and Serra, J. (2003). Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics 19, 553-562. 3/2003
• Jain, A. N., Tokuyasu, T. A., Snijders, A. M., Segraves, R., Albertson, D. G., and Pinkel, D. (2002). Fully automatic quantification of microarray image data. Genome Research 12, 325-332. (Full Text) 2/2002
• Yang, Y. H., Buckley, M. J., Dudoit, S., and Speed, T. P. (2002). Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics 11, 108-136. 1/2002
  [Explains the strategy behind the Spot software for analysing microarray images. Compares with several other commercial programs.]
• Yang, Y. H., Buckley, M. J., and Speed, T. P. (2001). Analysis of microarray images. Briefings in Bioinformatics 2, 341-349. 12/2001
• Steinfath, M., Wruck, W., Seidel, H., Lehrach, H., Radelof, U., and O'Brien, J. (2001). Automated image analysis for array hybridization experiments. Bioinformatics 17, 634-641. 7/2001
• Buhler, J., Ideker, T., and Haynor, D. (2000). Dapple: improved techniques for finding spots on DNA microarrays. Technical Report UWTR 2000-08-05, Department of Computer Science and Engineering, University of Washington, Seattle. 8/2000
  [Adaptive circle segmentation.]
• Chen, Y., Dougherty, E. R., and Bittner, M. L. (1997). Ratio based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics 2, 364-374.

Software Manuals

• QuantArray Analysis Software. http://lifesciences.perkinelmer.com.
• Scanalytics MicroArray Suite. http://www.scanalytics.com.
• GenePix Pro Users Guide, Axon Instruments Inc, Union City, CA. http://www.axon.com.
• ArrayVision, Imaging Research Inc. http://imaging.brocku.ca.
• Buckley, M. J. (2000). Spot User's Guide. CSIRO Mathematical and Information Sciences, Sydney, Australia. http://www.cmis.csiro.au/iap/Spot/spotmanual.htm.
• Eisen, M. B. (1999). ScanAlyze User Manual. Stanford University, Palo Alto, http://rana.lbl.gov. (Full text)

Supplementary References

• Soille, P. (1999). Morphological Image Analysis: Principles and Applications. Springer, New York.
• Adams, R., and Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641-647.
• Beucher, S., and Meyer, F. (1993). The morphological approach to segmentation: the watershed transformation. Mathematical morphology in image processing. Optical Engineering 34, 433-481.

Quality Measures

• Kreil, D. P., and MacKay, D. J. C. (2002). Reproducibility assessment of independent component analysis of expression ratios from DNA microarrays. Technical Report, Cavendish Laboratory, University of Cambridge. 10/2002
• Kothapalli, T., Yoder, S. J., Mane, S. and Loughran Jr, T. P. (2002). Microarray results: how accurate are they? BMC Bioinformatics 3, 22.
  [Examples of some inconsistencies.]
• Colantuoni, C., Henry, G., Zeger, S., and Pevsner, J. (2002). Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatically systematic hybridization artifacts. Biotechniques 32, 1316-1320. (Abstract) 6/2002
• Jenssen, T. K., Langaas, M., Kuo, W. P., Smith-Sorensen, B., Myklebost, O., and Hovig, E. (2002). Analysis of repeatability in spotted cDNA microarrays. Nucleic Acids Research 30, 3235-3244. 7/2002
• Sawitski, G. (2002). Quality control and early diagnostics for cDNA microarrays. R News 2/1, 6-10. (R News) 3/2002
  [Spatial plots of ranked foreground and background intensities.]
• Finkelstein, D., Ewing, R., Gollub, J., Sterky, F., Cherry, J. M., Somerville, S. (2002). Microarray data quality analysis: lessons from the AFGC project. Plant Molecular Biology 48, 119-132. 1/2002
• Yang, M. C., Ruan, Q.-G., Yang, J. J., Eckenrode, S, Wu, S., McIndoe, R. A., and She, J.-X. (2001). A statistical procedure for flagging weak spots greatly improves normalization and ratio estimates in microarray experiments. Physiological Genomics 7, 45-53. 8/2001
  [Proposes an explicit model multiplicative plus additive background model for spot intensities and attempts to validate the model using experimental data and simulations. The validation methods are not clear to me. Uses global normalization.]
• Wang, X., Ghosh, S., and Guo, S.-W. (2001). Quantitative quality control in microarray image processing and data acquisition. Nuclei Acids Research 29 (15), e75. 6/2001
  [Measure spot quality using a composite index involving size of spots, signal to noise ratio, level and heterogeneity of background and saturation of pixels. Demonstrate graphically an increasing trend relationship between spot variance and the quality measure.]
• Brown, C. S., Goodwin, P. C., and Sorger, P. K. (2001). Image metrics in the statistical analysis of DNA microarray data. PNAS 98, 8944-8949. 5/2001
• Tseng, G. C., Oh, M.-K., Rohlin, L., Liao, J. C., and Wong, W. H. (2001). Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects.  Nucleic Acids Research 29, 2549-2557. 4/2001
  [Filter out genes using a quality index computed from duplicate spots on the same slide.]
• Nadon, R., Shi, P., Skandalis, A., Woody, E., Hubschle, H., Susko, E., Rghei, N., and Ramm, P. (2001). Statistical methods for gene expression arrays. In Microarrays: Optical Technologies and Informatics, M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), Proceedings of SPIE, Vol. 4266, pp. 46-55.
  [Rejects spots which are outliers in a series of replicates. Outliers are judged relative to a normal distribution for the log-ratios, as judged by 3rd and 4th moments of the distribution. Rejects genes that have more than 2 outliers.]
• Kadota, K., Miki, R., Bono, H., Shimizu, K., Okazaki, Y. and Hayashizaki, Y. (2001), Preprocessing implementation for microarray (PRIM): an efficient method for processing cDNA microarray data. Physiological Genomics 4, 183-188. 1/2001
  [Filter spots based on an optimized threshold.]
• Buhler, J., Ideker, T., and Haynor, D. (2000). Dapple: improved techniques for finding spots on DNA microarrays. University of Washington CSE Technical Report UWTR 2000-08-05. 8/2000
  [Rejects or accepts spots based on brightness and position of centre within vignette.]
• Beißbarth, T., Fellenberg, K., Brors, B., Arribas-Prat, R., Boer, J. M., Hauser, N. C., Scheideler, M., Hoheisel, J. D., Schütz, G., Poustka, A., and Vingron M. (2000). Processing and quality control of DNA array hybridization data. Bioinformatics 16, 1014-1022. 11/2000
  [Nylon membrane microarrays. Flag spots on the basis of observed repeatability.]

Normalization

• Fan, J., Tam, P., Woude, G. V., and Ren, Y. (2004). Normalization and analysis of cDNA microarrays using within-array replications applied to neuroblastoma cell response to a cytokine. PNAS 101, 1135-1140. (Journal)  3/2/2004
• Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. In: METHODS: Selecting Candidate Genes from DNA Array Screens: Application to Neuroscience, D. Carter (ed.). To appear. (PDF) 4/2003
• Yang, Y. H., and Thorne, N. P. (2003). Normalization for two-color cDNA microarray data. In: D. R. Goldstein (ed.), Science and Statistics: A Festschrift for Terry Speed, IMS Lecture Notes - Monograph Series, Volume 40, pp. 403-418.
  [First description of single-channel normalization for cDNA arrays.]
• Colantuoni, C., Henry, G., Zeger, S., and Pevsner, J. (2002). SNOMAD (Standardization and Normalization of Microarray Data): web accessible gene expression data analysis. Bioinformatics 18, 1540-1541.
• Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., and Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96-S104. 7/2002
• Durbin, B. P., J.S. Hardin, J. S., Hawkins, D. M., and Rocke, D. M. (2002). A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics 18, S105-S110. 7/2002
• Finkelstein, D. B., Gollub, J., Ewing, R., Sterky, F., Somerville, S., and Cherry, J. M. (2001). Iterative linear regression by sector. In: Methods of Microarray Data Analysis. Papers from CAMDA 2000. eds. S. M. Lin and K. F. Johnson, Kluwer Academic, pp. 57-68. 10/2001
  [Normalization based on robust regression.]
• Schadt, E. E., Li, C., Ellis, B., and Wong, W. H. (2002). Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. Journal of Cellular Biochemistry 84, S37, 120-125.
  [Affymetrix. Find an invariant set of genes.]
• Dudoit, S., and Yang (2002), Y. H. Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software, Springer, New York (To appear).
• Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., and Speed, T. P. (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30(4):e15. 3/2002
  [Gives a more complete exposition of the normalization methods from Yang, Dudoit, Luu and Speed (2001). Proposed a more refined normalization method based on a weighted average of the within-tip loess normalization using all the genes and the global loess using a control cDNA titration series.]
• Kooperberg, C., Fazzio, T. G., Delrow, J. J., and Tsukiyama, T. (2002). Improved background correction for spotted cDNA microarrays. Journal of Computational Biology 9, 55-66. 1/2002
  [An empirical Bayes method of estimating the true signal intensities given foreground and background measurements. Eliminates negative corrected intensities.]
• Yang, Y. H., Dudoit, S., Luu, P., and Speed, T. P. (2001). Normalization for cDNA microarray data. In Microarrays: Optical Technologies and Informatics, M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), Proceedings of SPIE, Vol. 4266, pp. 141-152.
• Goryachev, A. B., MacGregor, P. F., and Edwards, A. M. (2001). Unfolding of microarray data. Journal of Computational Biology 8, 443-461.
  [The term 'unfolding' is from an analogy with particle physics, but the methods recommended in this paper are more straightforward than that would suggest. Suggestions include (i) that spots with high background values should be filtered out before other analysis, (ii) that local background estimates should be smoothed using a moving window of 5x5 or 7x7 spots, and (iii) that dye-swap experiments should be used to filter out spots with marked dye-specific hybridizations. One normalization method which is recommended is subtraction of robust means of the log-ratios for the print-tip groups, i.e., a simple robust sub-array normalization method.]
• Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H. and Herzel, H. (2000). Normalization strategies for cDNA microarrays. Nucleic Acids Research 28, No.10, e47.
• Hegde, P., Qi, R., Abernathy, K., Gay, C., Dharap, S., Gaspard, R., Hughes, J.E., Snesrud, E., Lee, N. and Quackenbush, J. (2000) A concise guide to cDNA microarray analysis. BioFeature, BioTechniques 29, 548-562.  9/2000
• Liao, B., Hale, W., Epstein, C. B., Butow, R. A., and Garner, H. R. (2000). MAD: a suite of tools for microarray data management and processing. Bioinformatics 16, 946-947.
• Eickhoff, B., Korn, B., Schick, M., Poustka, A. and van der Bosch, J. (1999). Normalization of array hybridization experiments in differential gene expression analysis. Nucleic Acids Research 27, e33. 11/1999
• Chen, Y., Dougherty, E. R., and Bittner, M. L. (1997). Ratio based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics 2, 364-374.
  [Perhaps the first statistical treatment of cDNA microarray data analysis.]

Normalization of Oligonucleotide Array Data

• Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., and Speed, T. P. (2003). Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264. 4/2003
• Bolstad, B. M., Irizarry R. A., Astrand, M., and Speed, T. P. (2003), A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185-193.
• Irizarry, R. A., Bolstad, B. M., Francois Collin, F., Cope, L. M., Hobbs, B., and Speed, T. P. (2003), Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 31(4):e15
• Hoffmann, R., Seidl, T., Dugas, M. (2002). Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis Genome Biology 2002 3(7): research0033.1-0033.11 6/2002
• Li, C., and Wong, W. H. (2001). Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proceedings of the National Academy of Sciences 98, 31-36. 1/2001

Normalization of Clontech Arrays

• Kepler, T. B., Crosby, L., Morgan, K. T. (2002). Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biology 3(7): research0037.1-0037.12 6/2002
  [Clontech are membrane arrays. Basic idea of intensity dependent normalization is similar to Yang, Dudoit, Luu and Speed (2001) for cDNA glass arrays.]

Calibration

• Barczak, A., Rodriguez, M. W., Hanspers, K., Tai, Y. C., Bolstad, B. M., Speed, T. P., and Erle, D. J. (2002). Spotted long oligonucleotide arrays for human gene expression analysis. UCSF, San Francisco, CA.
• Kothapalli, R., Yoder, S. J., Mane, S., and Loughran Jr, T. P. (2002). Microarray results: how accurate are they? BMC Bioinformatics 3, 22.
• Kuo, W. P., Jenssen, T. K., Butte, A. J., Ohno-Machado, L., and Kohane, I. S. (2002). Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18, 405-412.
• Li, J., Pankratz, M., and Johnson, J. A. (2002). Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays. Toxicol. Sci. 69, 383-390.
• Yuen, T., Wurmbach, E., Pfeffer, R. L., Ebersole, B. J., and Sealfon, S. C. (2002). Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Research 30, e48. 3/2002
• Ramdas, L., Coombes, K. R., Baggerly, K., Abruzzo, L., Highsmith, W. E., Krogmann, T., Hamilton, S. R., and Zhang, W. (2001). Sources of nonlinearity in cDNA microarray expression measurements. Genome Biology 2(11):research0047.1-0047.7. 10/2001
  [Quenching.]
• Taniguchi, M., Miura, K., Iwao, H., and Yamanaka, S. (2001). Quantitative assessment of DNA microarrays - comparison with Northern blot analyses. Genomics 71, 34-39.
  [Shows in a small experiment that the microarray gives lower estimated fold changes than Northern blot for many genes. Also demonstrates a dye effect for several genes.]
• Yue, H., Eastman, P. S., Wang, B. B., Minor, J., Doctolero, M. H., Nuttall, R. L., Stack, R., Becker, J. W., Montgomery, J. R., Vainer, M. and Johnston, R. (2001) An evaluation of the performance of cDNA microarrays for detecting changes in global gene expression. Nucleic Acids Research 29, e41.
  [Incyte arrays. Spikes in known fold changes with a range of known DNA concentrations.]
• Evertsz, E., Starink, P., Gupta, R. and Watson, D. (2000). Technology and applications of gene expression microarrays. In: Microarray Biochip Technology, M. Schena (ed.), Eaton Publishing, Natick, MA, pp. 149-166.
  [Shows amongst other things that microarrays under-estimate true differential expression ratios.]

Differential Expression - non-Bayes

• Ge, Y., Dudoit, S., and Speed, T. P. (2003). Resampling-based multiple testing for microarray data analysis, with discussion. TEST 12, 1-78. 6/2003
• Broberg, P. (2003). Statistical methods for ranking differentially expressed genes. Genome Biology 4: R41. 5/2003
• Ferkingstad, E., Langaas, M., and Lindqvist, B. (2003). Estimating the proportion of true null hypotheses, with application to DNA microarray data. Preprint Statistics No. 4/2003, Norwegian University of Science and Technology, Trondheim, Norway. http://www.math.ntnu.no/preprint/ 4/2003
• Park, T., Yi, S.-G., Lee, S., Lee, S. Y., Yoo, D.-H., Ahn, J.-I., and Lee, Y.-S. (2003). Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19, 694-703. 2/2003
  [Single channel ANOVA with permutation test.]
• Reiner, A., Yekutieli, D., and Benjamini, Y. (2003). Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368-375. 2/2003
• Cui, X., and Churchill, G. A. (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biology 4, 210.1-210.9. 3/2003
• Dudoit, S., Shaffer, J. P., and Boldrick, J. C. (2002). Multiple hypothesis testing in microarray experiments. Tech report #110, Division of Biostatistics, University of California, Berkeley.
• Storey, J. D., and Tibshirani, R. (2003). Statistical significance for genome-wide experiments. Technical Report, University of California, Berkeley. 1/2003.
• Yang, I. V., Chen, E., Hasseman, J. P., Liang, W., Frank, B. C., Wang, S., Sharov, V., Saeed, A. I., White, J., Li, J., Lee, N. H., Yeatman, T. J., Quackenbush, J. (2002). Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biology 3(11): research0062.1-0062.12. 10/2002
• Strand, A. D., Olsen, J. M., and Kooperberg, C. (To appear). Estimating confidence intervals for gene expression changes observed with oligonucleotide arrays. Human Molecular Genetics Sept 15. 9/2002
• Kooperberg, C., Sipione, S., LeBlanc, M. L., Strand, A. D., Cattaneo, E., and Olson, J. M. (To appear). Evaluating test-statistics to select interesting genes in microarray experiments. Human Molecular Genetics Sept 15. 9/2002
• Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society B 64.  9/2002
• Dudoit, S., Yang, Y. H, Callow, M. J., and Speed, T. P. (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12, 111-140. (Tech report #578) 1/2002
  [Explains how normalization - both mean and variance - for each gene cy3-cy5 pair should depend on the average intensity. Uses the Westfall and Young step-down permutation method to assess significance of gene-specific t tests. Controls family-wise error rate.]
• Storey, J. D., and Tibshirani, R. (2001). Estimating false discovery rates under dependence with applications to DNA microarrays. Technical Report 2001-28, Department of Statistics, Stanford University.
  [This technical report is effectively replaced by “Statistical inference for genome-wide experiments”, see above.]
• Pan, W. (2001). A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics. To appear. (Report 2001-028, Division of Biostatistics, University of Minnesota) 10/2001
• Loguinov, A. V., Anderson, L. M., Crosby, G. J., and Yukhananov, R. Y. (2001). Gene expression following acute morphine administration. Physiological Genomics 6, 169-181. 7/2001
  [A single slide method assuming that the log-intensities are multi-normal, independent and identically distributed. Regresses red channel log-intensities on green channel intensities and tries to identify differentially expressed genes as outliers in this regression. This seems an indirect and approximate way to try to reproduce what a quantile plot of the log-ratios would do more directly.]
• Boer, J. M., Huber, W. K., Sültmann, H., Wilmer, von Heydebreck, A., Haas, S., Korn, B., Gunawan, B., Vente, A., Füzesi, L., Vingron, M., and Poustka, A. (2001). Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Research 11, 1861-1870.
• Pan, W., Lin, J. and Le, C. (2001). A mixture model approach to detecting differentially expressed genes with microarray data. (Report 2001-011, Division of Biostatistics, University of Minnesota) 6/2001
• Tusher, V. G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98, 5116-5121. 4/2001
  [t-test for differential expression with offset added to standard error. Permutation method for estimating false discovering rate.]
• Newton, M. A., Kenziorski, C. M., Richmond, C. S., Blattner, F. R., and Tsui, K. W. (2001). On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8, 37-52. (Abstract, data and software)
  [A single slide method assuming gamma distributions for the log-intensities.]
• Ideker, T., Thorsson, V., Siegel, A. F., and Hood, L. (2000). Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. Journal of Computational Biology 7 (6) 805-817.
  [Methodology supporting VERA and SAM.]
• Manduchi, E., Grant, G. R., McKenzie, S. E., Overton, G. C., Surrey, S., and Stoeckert, Jr. C. J. (2000). Generation of patterns from gene expression data by assigning confidence to differentially expressed genes. Bioinformatics 16, 685-698. 7/2000
• MJ van der Laan, J Bryan: Gene Expression Analysis with the Parametric Bootstrap. Preprint, 1999.
• Westfall, P. H., and Young, S. S. (1993). Re-Sampling Based Multiple Testing. Wiley, New York.
  [Step-down adjusted P-value methods to control family-wise error rates. Not microarray specific.]

Differential Expression - Bayes and Empirical Bayes

• Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3. (Journal, Tech Report PDF) 2/2004
• Kendziorski, C. M., Newton, M. A., Lan, H., and Gould, M. N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22, 3899-3914. 30/12/2003.
• Efron, B. (2003). Robbins, empirical Bayes and microarrays. Annals of Statistics 31, 366-378. 4/2003
• Newton, M.A., and Kendziorski, C. M. (2003). Parametric empirical Bayes methods for microarrays. In: The analysis of gene expression data: methods and software, G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger (eds), Springer, New York, 2003. 3/2003
• Hung, S.-P., Baldi, P., and Hatfield, G. W. (2002). Global gene expression profiling in Escherichia coli K12. The effects of leucine-responsive regulatory protein. Journal of Biological Chemistry 277, 40309-40323. 18/7/2002
  [Application and exposition of Baldi and Long (2001)'s cyberT method.]
• Lönnstedt, I. and Speed, T. P. (2002). Replicated microarray data. Statistica Sinica 12, 31-46. 1/2002
  [An empirical Bayes method which estimates the odds that each gene is differentially expressed given replicated two-colour arrays.]
• Efron B., Tibshirani, R., Storey J. D., and Tusher V. (2001). Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 96, 1151-1160. 12/2001
  [Avoids parametric assumptions as far as possible.]
• Efron, B., Storey, J. D., and Tibshirani, R. (2001). Microarrays, empirical Bayes, and false discovery rates. Department of Statistics, Stanford University, 24 July 2001.
  [A connection between non-parametric empirical Bayes and FDR.]
• Baldi, P., and Long, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509-519. 6/2000
  [CyberT empirical Bayes method]
• Efron, B., Tibshirani, R., Goss, V., and Chu, G. (2000). Microarrays and their use in a comparative experiment. Department of Statistics, Stanford University, 30 October 2000.

Differential Expression - Bayes

• Townsend, J. P., and Hartl, D. L. (2002). Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments. Genome Biology 3(12): research0071.1-0071.16. 11/2002
• Parmigiani, G., Garrett, E.S., Anbazhaghan, R., Gabrielson, E. (2002) A statistical framework for expression-based molecular classification in cancer. Journal of the Royal Statistical Society B. In press. (PDF)
  [Fully Bayesian approach for oligonucleotide arrays.]
• Spang, R., Blanchette, C., Zuzan, H., Marks, J. R., Nevins, J., and West, M. (2002). Prediction and uncertainty in the analysis of gene expression profiles. In Silico Biology 2, 0033. 4/2002

Linear Models

• Kerr, M. K. (2003). Linear models for microarray data analysis: hidden similarities and differences. Journal of Computational Biology. To appear. 5/2003
• Díaz, E., Yang, Y. H., Ferreira, T., Loh, K. C., Okazaki, Y., Hayashizaki, Y., Tessier-Lavigne, M., Speed, T. P., and Ngai, J. (2003). Analysis of gene expression in the developing mouse retina. PNAS 100, 5491-5496. 29/4/2003
• Lin, D. M., Yang, Y. H., Scolnick, J. A., Brunet, L. J., Peng, V., Speed, T. P., and Ngai, J. (submitted). A spatial map of gene expression in the olfactory bulb. Department of Molecular and Cell Biology, University of California, Berkeley.
  [Uses six experimental conditions corresponding to a 3-dimensional spatial arrangement in the olfactory bulb. Uses direct comparison experimental design with linking. Shows superiority of the direct-comparison-design over the indirect all-versus-control design using a small data example. Estimates the between-condition contrasts using a robustly estimated linear model. Performs a 2-stage cluster analysis on the estimated-contrast-profiles. Validates the clustering using a randomization test. Selects individual genes for in situ hybridization based on their average spot intensity and homology to known sequences.]
• Diaz, E., Ge, Y., Yang, Y. H., Loh, K. C., Serafini, T. A., Okazaki, Y, Hayashizaki, Y, Speed, T. P., Ngai, J., Scheiffele, P. (2002). Molecular analysis of gene expression in the developing pontocerebellar projection system. Neuron 36, 417-434. (Full Text) 10/2002
  [Fits a linear model for each gene including interactions. Cluster analysis used on the linear model coefficients.]
• Lönnstedt, I., Grant, S., Begley, G., and Speed, T. P. (2001). Microarray analysis of two interacting treatments: a linear model and trends in expression over time. Technical Report, Department of Mathematics, Uppsala University, Sweden. 7/2001
  [Does a careful job of the interaction model.]
• 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. Genome Research 11, 1227-1236. 7/2001
  [Affymetrix. Uses weighted regression, z-values using GEE standard errors, then P-values from Hochberg modified Bonferroni.]
• West, M., Nevins, J. R., Marks, J. R., Blanchette, C., Spang R., and Zuzan, H. (2000). DNA microarray data analysis and regression modeling for genetic expression profiling. ISDS Discussion Paper 00-15.

Analysis of Variance

• Chu, T.-M., Weir, B., and Wolfinger, R. (2002). A systematic statistical linear modeling approach to oligonucleotide array experiments. Mathematical Biosciences 176, 35-51. 1/2002
• Kerr, M. K., Afshari, C. A., Bennett, L., Bushel, P., Martinez, J., Walker, N. J., and Churchill, G. A. (2002). Statistical analysis of a gene expression microarray experiment with replication. Statistica Sinica 12, 203-218. 1/2002
• Wolfinger, R. D., Gibson, G., Wolfinger, E. D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., and Paules, R. S. (2001). Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology 8, 625-637. 12/2001
  [Keeps channels separate as separate observations and includes a random effect for each spot to correlation the red and green observations on each spot. Assumes normality and independence for all the log-intensities. Normalizes log-intensities simply by subtracting mean for each array.]
• Jin, W., Riley, R. M., Wolfinger, R. D., White, K. P., Passador-Gurgel, G., and Gibson, G. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nature Genetics 29, 389 - 395. 12/2001
  [A separate channel analysis assuming normality for log-intensities.]
• Pritchard, C. C., Hsu, L., Delrow, J., and Nelson, P. S. (2001). Project normal: defining normal variance in mouse gene expression. PNAS 98, 13266-13271. 11/2001
  [Variance components for mouse and array within mouse.]
• Kerr, M. K., Leiter, P., and Churchill, G. A. (2001). Analysis of a designed microarray experiment, Proceedings of the IEEE-Eurasip Nonlinear Signal and Image Processing Workshop, June 3-6 2001.
• Kerr, M. K., Martin, M., and Churchill, G. A. (2000). Analysis of variance for gene expression microarray data. Journal of Computational Biology 7, 819-837. 12/2000

Missing Values

• Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525. 6/2001

Time Series

• Aach, J., and Church, G. M. (2001). Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495-508. 6/2001

Classification (aka Discrimination, Supervised Learning or Class Prediction)

• Dudoit, S., and Fridlyand, J. (2002). Classification in microaray experiments. In: T. P. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data. CRC Press.10/2002
• Spang, R., Zuzan, H., West, M., Nevins, J., Blanchette, C., Marks, J. R. (2002). Prediction and uncertainty in the analysis of gene expression profiles. In Silico Biology 2, 0033. (Full text) 4/2002
  [Bayesian model for tumor expression profiles. Uses probit regression for predictive classification, singular value decomposition for dimension reduction, and structured prior distributions to regularize the regression model.]
• Ibrahim, J. G., Chen, M.-H., and Gray, R. J. (2002). Bayesian models for gene expression with DNA microarray data. Journal of the American Statistical Society 97, 88-99. 3/2002
• Dudoit, S., Fridlyand, J., and Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77-87. 3/2002
• Pan, K.-H., Lih, C.-J. and Cohen, S. N. (2002). Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge. Proceedings of the National Academy of Sciences 99, 2118-2123. 2/2002
  [Describes GABRIEL software.]
• West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Marks, J. R., and Nevins, J. R. (2001). Predicting the clinical status of human breast cancer using gene expression profiles. Proceedings of the National Academy of Sciences 98, 11462-11467. 9/2001
  [Binary regression models combined with singular value decompositions and with stochastic regularization using Bayesian analysis.]
• Segal, E., Taskar, B., Gasch, A., Friedman, N., and Koller, D. (2001). Rich probabilistic models for gene expression. Bioinformatics 17, S243-S252. 6/2001
  [Probabilistic relational models.]
• Hastie, T., Tibshirani, R., Botstein, D., and Brown, P. (2001). Supervised harvesting of expression trees. Genome Biology 2 (1): research0003.1-0003.12. (Full text) 1/2001
• Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914. (Full Text) 10/2000
• Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares Jr, M., Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences 97, 262-267. 1/2000
• Hvidsten, T. R., Komorowski, J., Sandvik, A. K., and Laegreid, A. (2001). Predicting gene function from gene expressions and ontologies. Pacific Symposium on Biocomputing 6, 299-310. 1/2001
  [Genes are assigned to functional classes based on Ashburner's Gene Ontology. Learning method is rough set framework for rule induction. Implemented in the publicly avaialble ROSETTA toolkit. "We believe that the inclusion of domain knowledge is important in order to predict gene function from expression data; we do not believe that a syntactical analysis such as clustering utilises this resource well enough."]
• Califano, A., Stolovitzky, G., and Tu, Y. (2000). Analysis of gene expression microarrays for phenotype classification. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 75-85.
• West, M., Nevins, J., Marks, J., Spang, R., Blanchett, C. and Zuzan, H. (2000). DNA microarray data analysis and regression modeling for genetic expression profiling. http://www.stat.duke.edu/bioinformatics/bayes.html. 8/2000

Clustering (aka Unsupervised Learning or Pattern Discovery)

• Owen, A. B, Stuart, J. Mach, K., Villeneuve, A. M., and Kim, S. (2003). A gene recommender algorithm to identify co-expressed genes in C. elegans. Department of Statistics, Stanford University, CA. 6/2003
  [Rank genes according to how strongly they correlate with a set of query genes in those experiments for which the query genes are most strongly co-regulated.]
• Dudoit, S., and Fridlyand, J. (2003). Bagging to improve the accuracy of a clustering procedure. Bioinformatics. To appear.
• Chipman, H., Hastie, T., and Tibshirani, R. (2002). Clustering microarray data. In: T. P. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data. CRC Press. 10/2002
• Diaz, E., Ge, Y., Yang, Y. H., Loh, K. C., Serafini, T. A., Okazaki, Y, Hayashizaki, Y, Speed, T. P., Ngai, J., Scheiffele, P. (2002). Molecular analysis of gene expression in the developing pontocerebellar projection system. Neuron 36(3), 417-34. 10/2002
  [Clusters on the basis of coefficients estimated by a linear model.]
• Dudoit, S., and Fridlyand, J. (2002). A prediction-based resampling method to estimate the number of clusters in a dataset. Genome Biology 3(7), 0036.1-0036.21. 6/2002
• Tibshirani, R., Hastie, T., Narashiman B., and Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences 99, 6567-6572. 5/2002
• Parmigiani, G., Garrett, E. S., Anbazhagan, R., and Gabrielson, E. (2002). A statistical framework for expression-based molecular classification in cancer. Journal of the Royal Statistical Society B 64, 1-20. 3/2002
• McLachlan, G. J., Bean, R. W., and Peel, D. (2002). A mixture model-based approach to clustering of microarray expression data. Bioinformatics 18, 413-422.
  [Dimension reduction is achieved by fitting a mixture of factor analyzers.]
• Lazzeroni, L. and Owen, A. B. (2002). Plaid Models for Gene Expression Data. Statistica Sinica 12, 61-86. 1/2002
  [Cluster algorithm in which each gene can belong to more than one cluster or to no cluster depending on the characteristics on which the clustering is based.]
• Tibshirani, R. Hastie, T., Narasimhan, B., Eisen, M., Sherlock, G., Brown, P., and Botstein, D. (2002). Exploratory screening of genes and clusters from microarray experiments. Statistical Sinica 12, 47-60. 1/2002
  [Clustering as first stage in supervised learning strategy. Paper includes a review of the SAM method from Tusher et al (2001).]
• Pan, Wei, Lin, Jizhen, and Le, Chap T. (2002). Model-based cluster analysis of microarray gene-expression data. Genome Biology 3(2): research0009.1-0009.8. 1/2002 (PDF, Data)
• Fridlyand, J., and Dudoit, S. (2001). Applications of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method. Technical Report 600, Department of Statistics, University of California, Berkeley. 9/2001
• Tibshirani R, Walther G, Botstein D, and Brown P. (2001). Cluster validation by prediction strength. Technical Report, Department of Statistics, Stanford University. 9/2001
  [Validating the number of clusters in the data.]
• Xing, E. P., and Karp, R. M. (2001). CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics 17, S306-S315.
• Ewing, R. M., and Cherry, J. M. (2001). Visualization of expression clusters using Sammon's non-linear mapping. Bioinformatics 17, 658-659. 7/2001
• Lukashin, A. V., and Fuchs, R. (2001). Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters. Bioinformatics 17, 405-414. 5/2001
• Yeung, K. Y., Haynor, D. R., and Ruzzo, W. L. (2001). Validating clustering for gene expression data. Bioinformatics 17, 309-318. 4/2001
• Herrero, J., Valencia, A., and Dopazo, J. (2001). A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17, 126-136. 2/2001
• Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., Brown, P. (2000). “Gene shaving” as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology 1 (2): research0003.1-0003.21. (Full Text) 8/2000
• Alter, O., Brown, P.O., and Botstein, D. 2000. Singular value decomposition for genome-wide expression data processing and modeling. Proceedings of the National Academy of Sciences 97, 10101-10106. (Full Text) 6/2000
• Kerr, M. K., and Churchill, G. A. (2000). Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proceedings of the National Academy of Sciences 98, 8961-8965.
• Alter, O., Brown, P., and Botstein, D. (2000). Singular value decomposition for genome-wide expression data processing and modeling. Proceedings of the National Academy of Sciences 97, 10101-10106.
• Tibshirani, R., Hastie, T. Eisen, M., Ross, D. , Botstein, D. and Brown, P. (1999). Clustering methods for the analysis of DNA microarray data. Stanford Tech. report Oct. 1999. (Postscript, Compressed Postscript) 8/1999
• Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., and Lander, E. S. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537. (Full Text) 8/1999
• Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q. , Kitareewan, S., Dmitrovsky, E., Lander, E. S., and Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences 96, 2907-2912. (Full Text) 3/1999
• Ben-Dor, A., Shamir, R., Yakhini, Z. (1999). Clustering gene expression patterns. Journal of Computational Biology 6, 281-297.
• Eisen, M. B., Spellman, P. T., Brown, P. O., Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences 95, 14863-14868. (Full Text) 12/1998

Case Studies

• X. Chen, S. T. Cheung, S. So, S. T. Fan, C. Barry, J. Higgins, K.-M. Lai, J. Ji, S. Dudoit, I. O. L. Ng, M. van de Rijn, D. Botstein, and P. O. Brown (2002). Gene expression patterns in human liver cancers. Molecular Biology of the Cell (In press).
• J. C. Boldrick, A. A. Alizadeh, M. Diehn, S. Dudoit, C. L. Liu, C. E. Belcher, D. Botstein, L. M. Staudt, P. O. Brown, and D. A. Relman (2002). Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc. Natl. Acad. Sci.. Vol. 99, No. 2, p. 972--977.
• Lin, D. M., Yang, Y. H., Scolnick, J. A., Brunet, L. J., Peng, V., Speed, T. P., and Ngai, J. (submitted). A spatial map of gene expression in the olfactory bulb. Department of Molecular and Cell Biology, University of California, Berkeley.
  [Uses six experimental conditions corresponding to a 3-dimensional spatial arrangement in the olfactory bulb. Uses direct comparison experimental design with linking. Shows superiority of the direct-comparison-design over the indirect all-versus-control design using a small data example. Estimates the between-condition contrasts using a robustly estimated linear model. Performs a 2-stage cluster analysis on the estimated-contrast-profiles. Validates the clustering using a randomization test. Selects individual genes for in situ hybridization based on their average spot intensity and homology to known sequences.]
• Therese Sorlie, Perou, C., Robert Tibshirani, Turid Aas, Stephanie Geisler, Hilde Johnsenb, Trevor Hastie, Michael B. Eisenh, Matt van de Rijn, Stefanie S. Jeffrey, Thor Thorsen, Hanne Quist, John C. Matese, Patrick O. Brown, David Botstein, Per Eystein Lonninngg, and Anne-Lise Borresen-Daleb. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98: 10869-10874.
• Colantuoni C, Jeon OH, Hyder K, Chenchik A, Khimani AH, Narayanan V, Hoffman EP, Kaufmann WE, Naidu S, Pevsner J. (2001). Gene expression profiling in postmortem Rett Syndrome brain: differential gene expression and patient classification. Neurobiol Dis 8, 847-865. 10/2001
  [Clustering.]
• S. Ishida, E. Huang, H. Zuzan, R. Spang, G. Leone, M. West and J.R. Nevins (2001). Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis. Molecular and Cellular Biology 21, 4684-99.
• Callow, M. J., Dudoit, S., Gong, E. L., Speed, T. P., and Rubin, E. M. (2000). Microarray expression profiling identifies genes with altered expression in HDL deficient mice. Genome Research 10, 2022-2029. (Full Text)
• Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Seftor, E., Hendrix, M., Radmacher, M., Simon, R., Yakhini, Z., Ben-Dor, A., Sampas, N., Dougherty, E., Wang, E., Marincola, F., Gooden, C., Lueders, J., Glatfelter, A., Pollock, P., Carpten, J., Gillanders, E., Leja, D., Dietrich, K., Beaudry, C., Berens, M., Alberts, D., Sondak, V., Hayward, N., Trent, J. (2000). Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536-540.
  [Clustering using hierarchical dendrograms, multidimensional scaling, and Cluster Affinity Search Technique.]
• Alizadeh, A. A, Eisen, M. B, Davis, R. E, Ma, C, Lossos, I. S, Rosenwald, A, Boldrick, J. C, Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Hudson, J. Jr, Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Levy, R., Wilson, W., Grever, M. R., Byrd, J. C., Botstein, D., Brown, P. O., Staudt, L. M. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769), 503-511. 2/2000
  [Cluster new diseases types using hierarchical clustering.]

Platform Comparisons

• Mah, N., Thelin, A., Lu, T., Nikolaus, S., Kühbacher, T., Gurbuz, Y., Eickhoff, H., Klöppel, G., Lehrach, H., Mellgård, B., Costello, C. M., and Schreiber, S. (2004) A comparison of oligonucleotide and cDNA-based microarray systems. Physiol. Genomics 16, 361-370. First published November 25, 2003.

Synthesized Oligonucleotide Arrays

• Li, C., Tseng, G., and Wong, W. H. (2002). Model-based analysis of oligonucleotide arrays and issues in cDNA microarray analysis. In: T. P. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data. CRC Press.10/2002