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References
Other Bibliographies
Reviews and Collections
- Hand, D. J., and Heard, N. A. (2005). Finding groups in gene expression
data. Journal of Biomedicine and Biotechnology 2005:2, 215-225.
- Eddy, S. R. (2004). What is Bayesian statistics? Nature Biotechnology
22, 1177-1178.
- 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.]
- 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.
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
Gene Set Tests
- Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag,
S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E.,
Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo,
P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., Groop, L.
C. (2003). PGC-1a-responsive genes involved in
oxidative phosphorylation are coordinately downregulated in human diabetes.
Nature Genetics 34, 267-273. (Published online 15 June 2003)
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
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