Clustering and visualization approaches for human cell cycle gene expression data analysis

International Journal of Approximate Reasoning

Volumn 47, Issue 1, 2008, Pages 70-84

  Napolitano, F ^a   Raiconi, G ^a   Tagliaferri, R ^a   Ciaramella, A ^b   Staiano, A ^b   Miele, G ^c,d  

^a UNIVERSITY OF SALERNO   (Italy)

^b UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

^c UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^d INFN SEZIONE DI NAPOLI   (Italy)

Author keywords

Data analysis; Data visualization; Microarray data; Preprocessing analysis

Indexed keywords

AGGLOMERATION; DATA MINING; DATA REDUCTION; MICROARRAYS; NEURAL NETWORKS; PROBABILISTIC LOGICS; THREE DIMENSIONAL; TWO DIMENSIONAL;

MULTI DIMENSIONAL SCALING APPROACH; NOISY DATA; PREPROCESSING ANALYSIS;

GENE EXPRESSION;

EID: 36248961189     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2007.03.013     Document Type: Article

References (41)

1. Alter O., Brown P.O., and Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97 (2000) 10101-10106
2. Amato R., Ciaramella A., Deniskina N., et al. A multi-step approach to time series analysis and gene expression clustering. Bioinformatics 22 5 (2006) 589-596
3. Ando T., Suguro M., Hanai H., and Seto M. Fuzzy neural network applied to gene expression profiling for producing the prognosis of diffuse large B-cell lymphoma. Cancer Res. 93 (2002) 1207-1212
4. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press
5. Bishop C.M., Svensen M., and Williams C.K.I. GTM: the generative topographic mapping. Neural Computation 10 1 (1998) 215-234
6. Bussermaker H.J., Li H., and Siggia E.D. Regulatory element detection using correlation with expression. Nat. Genet. 27 (2001) 167-174
7. Chang J.H., Chi S.W., and Zhang B.T. Gene expression pattern analysis via latent variable models coupled with topographic clustering. Genomics Inform. 1 (2003) 32-39
8. Chang K., and Ghosh J. A unified model for probabilistic principal surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 23 1 (2001)
9. Chen Y., Dougherty E.R., and Bittner M.L. Ratio-based decision and the quantitative analysis of cDNA microarray images. J. Biomed. Opt. (1997) 364-374
10. Cho R.J., Cambell M.J., Winzeler E.A., Steinmerz L., Conway A., Wodicka L., Wolfsberg T.G., Gabrielian A.E., Landsman D., Lockhard D., and Davis R.W. A genom-wide transcriptional analysis of the mitotic cells. Mol. Cell 2 1 (1998) 65-73
11. Christie K.R., et al. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. (2004) 32 Database issue: D311-4
12. Ciaramella A., Longo G., Staiano A., and Tagliaferri R. NEC: a hierarchical agglomerative clustering based on Fisher and Negentropy information. Lect. Notes Comp. Sci. 3931 (2006) 49-56
13. Ciaramella A., Bongardo C., Aller H.D., Aller M.F., De Zotti G., Lähteenmaki A., Longo G., Milano L., Tagliaferri R., Teräsranta H., Tornikoski M., and Urpo S. A multifrequency analysis of radio variability of blazars. Astron. Astrophys. J. 419 (2004) 485-500
14. de Lichtenberg U., Jensen L.J., Fausbøll A., Jensen T.S., Bork P., and Brunak S. Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics 21 (2005) 1164-1171
15. Dempster A.P., Laird N.M., and Rubin D.B. Maximum-likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. 39 1 (1977)
16. di Bernardo D., Thompson M.J., Gardner T.S., Chobot S.E., Eastwood E.L., Wojtovich A.P., Elliott S.J., Schaus S.E., and Collins J.J. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23 3 (2005) 377-383
17. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), John Wiley & Sons Inc.
18. Eisen M.B., Spellman P.T., Brown P.O., and Botstein D. Clustering analysis and display of genome-wide expression patterns. PNAS USA 95 (1998) 14863-14868
19. Ermolaeva O., et al. Data management and analysis for gene expression arrays. Nat. Genet. 20 (1998) 19-23
20. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning (2001), Springer-Verlag, New York
21. Hughes T.R., et al. Functional discovery via a compendium of expression profiles. Cell 102 (2000) 109-126
22. Hyvärinen A., Karhunen J., and Oja E. Independent Component Analysis (2001), John Wiley & Sons
23. Karhunen J., and Joutsensalo J. Representation and separation of signals using non-linear PCA type learning. Neural Networks 7 (1994) 113-127
24. Karhunen J., and Joutsensalo J. Generalizations of principal component analysys, optimization problems and neural networks. Neural Networks 8 (1995) 549-563
25. Kerr M.K., and Churchill G.A. Statistical design and the analysis of gene expression microarray data. Genet. Res. 77 (2001) 123-128
26. Kohonen T. Self-Organizing Maps (1995), Springer-Verlag, Berlin
27. Liebermeister W. Linear modes of gene expression determined by independent component analysis. Bioinformatics 18 (2002) 51-60
28. Misra J., Schmitt W., Hwang D., Hsiao L., Gullans S., and Stephanopoulos G. Interactive exploration of microarray gene expression patterns in a reduced dimensional space. Genome Res. 12 (2002) 1112-1120
29. M. Mukherjee, P. Tamago, J.P. Mesirov, D. Slorim, A. Verni, T. Poggio, Support vector machine classification of microarray data, Technical Report, Cambridge: MIT, N.182, 1999.
30. Purdom E., and Holmes S.P. Error distribution for gene expression data. Stat. Appl. Genet. Mol. Biol. 4 1 (2005) 16
31. Robinson A. A new map projection: its development and characteristics. International Yearbook of Cartography 14 (1974) 145-155
32. Tagliaferri R., Ciaramella A., Milano L., Barone F., and Longo G. Spectral analysis of stellar light curves by means of neural networks. Astron. Astrophys. Suppl. Ser. 137 (1999) 391-405
33. Tagliaferri R., Pelosi N., Ciaramella A., Longo G., Milano L., and Barone F. Soft computing methodologies for spectral analysis in cyclostratigraphy. Comp. Geosci. 27 (2001) 535-548
34. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S., and 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
35. Törönen P., Kolehmainen M., Wong G., and Castrén E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451 (1999) 142-146
36. Townsend J.P., and Hartl D.L. Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple treatments or samples. Genome Biol. 3 (2002) research0071.1-0071.16
37. Townsend J.P. Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays. BMC Bioinform. 5 (2004) 54
38. Tseng G.C., Oh M.K., Rohlin L., et al. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res. 29 (2001) 2549-2557
39. Whitfield M.L., Sherlock G., Saldanha A.J., Murray J.I., Ball C.A., Alexander K.E., Matese J.C., Perou C.M., Hurt M.M., Brown P.O., and Botstein D. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13 (2002) 1977-2000
40. Wolfinger R.D., Gibson G., Wolfinger E., Bennett L., Hamadeh H., et al. Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8 (2001) 625-637
41. Yeung K.Y., Fraley C., Murua A., Raftery A.E., and Ruzzo W.L. Model based clustering and data transformations for gene expression data. Bioinformatics 17 (2001) 977-987