Analysis of large-scale gene expression data

Current Opinion in Immunology

Volumn 12, Issue 2, 2000, Pages 201-205

  Sherlock, Gavin ^a  

^a STANFORD UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEMENTARY DNA;

DATA ANALYSIS; GENE EXPRESSION; GENETIC ANALYSIS; RECOMBINANT DNA TECHNOLOGY; REVIEW;

EID: 0034062083     PISSN: 09527915     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0952-7915(99)00074-6     Document Type: Review

References (26)

1. DeRisi J.L., Iyer V.R., Brown P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 278:1997;680-686.
2. Lashkari D.A., DeRisi J.L., McCusker J.H., Namath A.F., Gentile C., Hwang S.Y., Brown P.O., Davis R.W. Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc Natl Acad Sci USA. 94:1997;13057-13062.
3. Schena M., Shalon D., Heller R., Chai A., Brown P.O., Davis R.W. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci USA. 93:1996;10614-10619.
4. Lockhart D.J., Dong H., Byrne M.C., Follettie M.T., Gallo M.U., Chee M.S., Mittmann M., Wang C., Kobayashi M., Horton H.et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 14:1996;1675-1680.
5. Wodicka L., Dong H., Mittmann M., Ho M.H., Lockhart D.J. Genome-wide expression monitoring in Saccharomyces cerevisiae. Nat Biotechnol. 15:1997;1359-1367.
6. Spellman P.T., Sherlock G., Zhang M.Q., Iyer V.R., Anders K., Eisen M.B., Brown P.O., Botstein D., Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 9:1998;3273-3297. Four independent sets of experiments (one from Cho et al. [22]) are analysed to provide a comprehensive picture of the cell-cycle-regulated transcripts in yeast.
7. Heyer L.J., Kruglyak S., Yooseph S. Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9:1999;1106-1115. The authors propose a novel correlation measurement, the jackknife correlation, that reduces the likelihood of false positives. They also describe a new clustering method, which doesn't suffer from some of the problems associated with other partitioning methods.
8. Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:1998;14863-14868. The use of an agglomerative hierarchical clustering method is described, with a useful false color representation of the resulting tree based structures. In addition software, for both analysis and visualization, is described that is freely available to academic researchers at http://rana.stanford.edu/software .
9. Wen X., Fuhrman S., Michaels G.S., Carr D.B., Smith S., Barker J.L., Somogyi R. Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci USA. 95:1998;334-339.
10. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA. 96:1999;6745-6750. This paper introduces a divisive clustering method for the analysis of colon cancer data. The notion of two dimensional clustering is also introduced, as a method whereby different cell lines and tissues can be compared with one another.
11. Clustering Software on World Wide Web URL: http://genome-www.stanford.edu/~sherlock/cluster.html Software for hierarchical clustering, using a novel node-switching algorithm to somewhat optimize node order, and making self-organizing maps and k-means clustering (with automatic clustering of each of the partitions) is freely available to academic researchers. In addition, a web-based SOM and k-means viewing program is also available.
12. Perou C.M., Jeffrey S.S., van de Rijn M., Rees C.A., Eisen M.B., Ross D.T., Pergamenschikov A., Williams C.F., Zhu S.X., Lee J.C.et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA. 96:1999;9212-9217.
13. Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J. Basic local alignment search tool. J Mol Biol. 215:1990;403-410.
14. Thompson J.D., Higgins D.G., Gibson T.J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22:1994;4673-4680.
15. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S., Golub T.R. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA. 96:1999;2907-2912. This paper introduces the use of self-organizing maps as a partitioning method for gene expression data.
16. Toronen P., Kolehmainen M., Wong G., Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451:1999;142-146.
17. Tavazoie S., Hughes J.D., Campbell M.J., Cho R.J., Church G.M. Systematic determination of genetic network architecture. Nat Genet. 22:1999;281-285.
18. Kohonen T. Self Organizing Maps. 1995;Springer, Berlin.
19. Everitt B. Cluster Analysis 122. 1974;Heinemann, London.
20. Milligan G.W., Cooper M.C. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 50:1985;159-179.
21. 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.et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 286:1999;531-537. A technique for classifying tumor samples based on expression data is described. The technique is robust and also provides a measurement to indicate the confidence of the assignment.
22. Cho R.J., Campbell M.J., Winzeler E.A., Steinmetz L., Conway A., Wodicka L., Wolfsberg T.G., Gabrielian A.E., Landsman D., Lockhart D.J., Davis R.W. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell. 2:1998;65-73.
23. Mews H.W., Frishman D., Gruber C., Geir B., Haase D., Kaps A., Lemcke K., Mannhaupt G., Pfeiffer F., Schuller C.et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 28:2000;37-40.
24. Karlin S., Altschul S.F. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc Natl Acad Sci USA. 87:1990;2264-2268.
25. Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:1997;3389-3402.
26. Brown M.P., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Furey T.S., Ares M. Jr., Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA. 97:2000;262-267.