Analysis of microarray data

Nutrigenomics

Volumn , Issue , 2005, Pages 43-66

  Mills, Ken ^a  

^a CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 70349813407     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (44)

1. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995; 270:467-470.
2. DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 1996; 14:457-460.
3. Hegde P, Qi R, Abernathy K, Gay C, Dharap S, Gaspard R et al. A concise guide to cDNA microarray analysis. Biotechniques 2000; 29(3):548-556.
4. Dobbin K, Simon R. Comparison of microarray designs for class comparison and class discovery. Bioinformatics 2002; 18(11):1438-1445.
5. Bilban M, Buehler LK, Head S, Desoye G, Quaranta V. Normalizing DNA microarray data. Curr Issues Mol Biol 2002; 4(2):57-64.
6. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19(2):185-193.
7. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001; 29(4):365-371.
8. Stoeckert CJ Jr, Causton HC, Ball CA. Microarray databases: standards and ontologies. Nat Genet 2002; 32(suppl):469-473.
9. Craigon DJ, James N, Okyere J, Higgins J, Jotham J, May S. NASCArrays: a repository for microarray data generated by NASC’s transcriptomics service. Nucl Acids Res 2004; 32(1):D575-D577.
10. Tong W, Cao X, Harris S, Sun H, Fang H, Fuscoe J et al. ArrayTrack—supporting toxicogenomic research at the US Food and Drug Administration National Center for Toxicological Research. Environ Health Perspect 2003; 111(15):1819-1826.
11. Killion PJ, Sherlock G, Iyer VR. The Longhorn array database (LAD): an open-source, MIAME compliant implementation of the Stanford Microarray Database (SMD). BMC Bioinformatics 2003; 4(1):32.
12. Taylor CF, Paton NW, Garwood KL, Kirby PD, Stead DA, Yin Z et al. A systematic approach to modeling, capturing, and disseminating proteomics experimental data. Nat Biotechnol 2003; 21(3):247-254.
13. Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 2003; 31(1):68-71.
14. Spellman PT, Miller M, Stewart J, Troup C, Sarkans U, Chervitz S et al. Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 2002; 3(9):RESEARCH0046.
15. Singhal S, Kyvernitis CG, Johnson SW, Kaiser LR, Liebman MN, Albelda SM. Microarray data simulator for improved selection of differentially expressed genes. Cancer Biol Ther 2003; 2(4):383-391.
16. Zhao Y, Pan W. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments. Bioinformatics 2003; 19(9):1046-1054.
17. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98(9):5116-5121.
18. Cardoso F. Microarray technology and its effect on breast cancer (re)classification and prediction of outcome. Breast Cancer Res 2003; 5(6):303-304.
19. Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci USA 2003; 100(17):9991-9996.
20. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling [see comments]. Nature 2000; 403(6769):503-511.
21. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286(5439):531-537.
22. Sarkar M, Leong TY. Fuzzy K-means clustering with missing values. Proc AMIA Symp 2001; 588-592.
23. Schachter AD, Kohane IS. An unsupervised self-optimizing gene clustering algorithm. Proc AMIA Symp 2002; 682-686.
24. Kohonen T, Somervuo P. How to make large self-organizing maps for nonvectorial data. Neural Netw 2002; 15(8-9):945-952.
25. Kohonen T. Comparison of SOM point densities based on different criteria. Neural Comput 1999; 11(8):2081-2095.
26. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 1999; 96(6):2907-2912.
27. Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics 2001; 17(9):763-774.
28. Remy I, Michnick SW. Dynamic visualization of expressed gene networks. J Cell Physiol 2003; 196(3):419-429.
29. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genomewide expression patterns. Proc Natl Acad Sci USA 1998; 95(25):14863-14868.
30. Alter O, Brown PO, Botstein D. Singular value decomposition for genomewide expression data processing and modeling. Proc Natl Acad Sci USA 2000; 97(18):10101-10106.
31. Shannon W, Culverhouse R, Duncan J. Analyzing microarray data using cluster analysis. Pharmacogenomics 2003; 4(1):41-52.
32. Guess MJ, Wilson SB. Introduction to hierarchical clustering. J Clin Neurophysiol 2002; 19(2):144-151.
33. Soinov LA. Supervised classification for gene network reconstruction. Biochem Soc Trans 2003; 31(Pt 6):1497-1502.
34. Lagreid A, Hvidsten TR, Midelfart H, Komorowski J, Sandvik AK. Predicting gene ontology biological process from temporal gene expression patterns. Genome Res 2003; 13(5):965-979.
35. Soinov LA, Krestyaninova MA, Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biol 2003; 4(1):R6.
36. Nelander S, Mostad P, Lindahl P. Prediction of cell type-specific gene modules: identification and initial characterization of a core set of smooth muscle-specific genes. Genome Res 2003; 13(8):1838-1854.
37. Bura E, Pfeiffer RM. Graphical methods for class prediction using dimension reduction techniques on DNA microarray data. Bioinformatics 2003; 19(10):1252-1258.
38. Tanay A, Sharan R, Shamir R. Discovering statistically significant biclusters in gene expression data. Bioinformatics 2002; 18(suppl 1):S136-S144.
39. Azuaje F. Clustering-based approaches to discovering and visualising microarray data patterns. Brief Bioinform 2003; 4(1):31-42.
40. Dudoit S, Gentleman RC, Quackenbush J. Open source software for the analysis of microarray data. Biotechniques 2003; (suppl):45-51.
41. Anderle P, Duval M, Draghici S, Kuklin A, Littlejohn TG, Medrano JF et al. Gene expression databases and data mining. Biotechniques 2003; (suppl):36-44.
42. Satagopan JM, Panageas KS. A statistical perspective on gene expression data analysis. Stat Med 2003; 22(3):481-499.
43. Tamames J, Clark D, Herrero J, Dopazo J, Blaschke C, Fernandez JM et al. Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction. J Biotechnol 2002; 98(2-3):269-283.
44. Weinstein JN, Scherf U, Lee JK, Nishizuka S, Gwadry F, Bussey AK et al. The bioinformatics of microarray gene expression profiling. Cytometry 2002; 47(1):46-49.