Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.
[1] M.B. Eisen, P.T. Spellman, , P.O. Brown, D. Botstein, ¨Cluster analysis
and display of genome-wide expression patterns¨. Proc. Natl Acad. Sci.
vol. 95, pp. 14863-14868, Dec. 1998.
[2] G.S. Michaels, D.B. Carr, M. Askenazi, S. Fuhrman, X. Wen, R.
Somogyi, ¨Cluster analysis and data visualization of large-scale gene
expression data¨. Pacific Symposium on Biocomputing vol. 3 pp. 42-53,
1998.
[3] P.T. Spellman, G. Sherlock, M.Q . Zhang, V.R . Iyer, K . Anders, M.B
Eisen, , P.O. Brown., D. Botstein, B. Futcher , ¨Comprehensive
identification of cell cycle-regulated genes of the yeast Saccharomyces
cerevisiae by microarray hybridization¨. Mol Biol Cell vol. 9 pp. 3273-
3297, Dec. 1998.
[4] P. D'haeseleer, S. Liang, R. Somogyi, ¨Genetic network inference: From
co-expression clustering to reverse engineering¨. Bioinformatics vol. 16
pp. 707-726, 2000.
[5] R.J. Cho, et al., ¨A genome-wide transcriptional analysis of the mitotic
cell cycle¨. Mol. Cell vol. 2 pp. 65-73, 1998.
[6] S. Chu, J. DeRisi, et al., ¨The transcriptional program of sporulation in
budding Yeast¨. Science vol. 282 pp. 699-705, 1998.
[7] J.L. DeRisi, V.R. Iyer, P.O. Brown, ¨Exploring the metabolic and
genetic control of gene expression on a genomic scale ¨. Science vol.
278 pp. 680-686, Oct. 1997.
[8] A. Basilevsky, ¨Statistical Factor Analysis and Related Methods¨,
Theory and Aplications, John Wiley & Sons, New York , 1994.
[9] S.G. Hilsenbeck, W.E. Friedrichs, R. Schi®, P. O'Connell, R.K. Hansen,
C.K. Osborne, S.A.W. Fuqua, ¨Statistical analysis of array expression
data as applied the problem of tamoxifen resistance¨. J Natl Cancer
Institute vol. 91 pp. 453-459, 1999.
[10] J. Vohradsky, X.M. Li, C.J. Thompson, ¨Identification of prokaryotic
developmental stages by statistical analyses of two-dimensional gel
patterns¨. Electrophoresis vol. 18 pp. 1418-1428, 1998.
[11] J.C. Craig, J.H. Eberwine, J.A. Calvin, B. Wlodarczyk, G.D. Bennett,
R.H. Finnell, ¨Developmental expression of morphoregulatory genes in
the mouse embryo: an analytical approach using a novel technology¨.
Biochem Mol Med vol. 60 pp. 81-91, 1997.
[12] K. Kucho, K. Okamoto, Y. Tsuchiya, S. Nomura,, M. Nango, M.
Kanehisa, M. Ishiura, ¨Global Analysis of Circadian Expression in the
Cyanobacterium Synechocystis sp. Strain PCC 6803¨. Journal Of
Bacteriology vol. 187 pp. 2190-2199, Dec. 2004.
[13] O. Alter, P.O. Brown, D. Botstein, ¨ Singular value decomposition for
genome-wide expression data processing and modeling¨. Proc. Natl
Acad. Sci. USA vol. 97 pp. 10101-10106, June 2000.
[1] M.B. Eisen, P.T. Spellman, , P.O. Brown, D. Botstein, ¨Cluster analysis
and display of genome-wide expression patterns¨. Proc. Natl Acad. Sci.
vol. 95, pp. 14863-14868, Dec. 1998.
[2] G.S. Michaels, D.B. Carr, M. Askenazi, S. Fuhrman, X. Wen, R.
Somogyi, ¨Cluster analysis and data visualization of large-scale gene
expression data¨. Pacific Symposium on Biocomputing vol. 3 pp. 42-53,
1998.
[3] P.T. Spellman, G. Sherlock, M.Q . Zhang, V.R . Iyer, K . Anders, M.B
Eisen, , P.O. Brown., D. Botstein, B. Futcher , ¨Comprehensive
identification of cell cycle-regulated genes of the yeast Saccharomyces
cerevisiae by microarray hybridization¨. Mol Biol Cell vol. 9 pp. 3273-
3297, Dec. 1998.
[4] P. D'haeseleer, S. Liang, R. Somogyi, ¨Genetic network inference: From
co-expression clustering to reverse engineering¨. Bioinformatics vol. 16
pp. 707-726, 2000.
[5] R.J. Cho, et al., ¨A genome-wide transcriptional analysis of the mitotic
cell cycle¨. Mol. Cell vol. 2 pp. 65-73, 1998.
[6] S. Chu, J. DeRisi, et al., ¨The transcriptional program of sporulation in
budding Yeast¨. Science vol. 282 pp. 699-705, 1998.
[7] J.L. DeRisi, V.R. Iyer, P.O. Brown, ¨Exploring the metabolic and
genetic control of gene expression on a genomic scale ¨. Science vol.
278 pp. 680-686, Oct. 1997.
[8] A. Basilevsky, ¨Statistical Factor Analysis and Related Methods¨,
Theory and Aplications, John Wiley & Sons, New York , 1994.
[9] S.G. Hilsenbeck, W.E. Friedrichs, R. Schi®, P. O'Connell, R.K. Hansen,
C.K. Osborne, S.A.W. Fuqua, ¨Statistical analysis of array expression
data as applied the problem of tamoxifen resistance¨. J Natl Cancer
Institute vol. 91 pp. 453-459, 1999.
[10] J. Vohradsky, X.M. Li, C.J. Thompson, ¨Identification of prokaryotic
developmental stages by statistical analyses of two-dimensional gel
patterns¨. Electrophoresis vol. 18 pp. 1418-1428, 1998.
[11] J.C. Craig, J.H. Eberwine, J.A. Calvin, B. Wlodarczyk, G.D. Bennett,
R.H. Finnell, ¨Developmental expression of morphoregulatory genes in
the mouse embryo: an analytical approach using a novel technology¨.
Biochem Mol Med vol. 60 pp. 81-91, 1997.
[12] K. Kucho, K. Okamoto, Y. Tsuchiya, S. Nomura,, M. Nango, M.
Kanehisa, M. Ishiura, ¨Global Analysis of Circadian Expression in the
Cyanobacterium Synechocystis sp. Strain PCC 6803¨. Journal Of
Bacteriology vol. 187 pp. 2190-2199, Dec. 2004.
[13] O. Alter, P.O. Brown, D. Botstein, ¨ Singular value decomposition for
genome-wide expression data processing and modeling¨. Proc. Natl
Acad. Sci. USA vol. 97 pp. 10101-10106, June 2000.
@article{"International Journal of Medical, Medicine and Health Sciences:61690", author = "Carla Layana Luis Diambra", title = "Dynamical Analysis of Circadian Gene Expression", abstract = "Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.
", keywords = "circadian rhythms, clustering, gene expression, PCA.", volume = "1", number = "5", pages = "306-5", }
