Project 3: Cluster analysis of gene expression data by pLdevf2h


									Project 3: Cluster analysis of gene expression data
Statistical methods for the analysis of gene expression data have been widely used in
cDNA experiments. In Dudoit, S., et al. (2002), a cDNA experiment was performed to
identify genes with altered expression in two mouse models (the apolipoprotein AI
knocked out mice and the scavenger receptor BI transgenic mice) with very low HDL
cholesterol levels compared to inbred control mice. Besides the identification of
significantly expressed genes using R package SMA , the participants in this project are
expected to use different clustering methods described in Eisen MB, et al. (1998)
(hierarchical clustering, self-organizing maps (SOMs), k-means clustering, principal
component analysis, and suitable model-based clustering methods) to classify the
significantly expressed genes. Clustering analysis of time-course gene expression data
(Cho, R., et al. (1998), Spellman PT, et al. (1998)), which identifies subsets of genes that
behave similarly along time under the set of experimental conditions, will also be
considered in this project. Participants are encouraged to use the public microarray and
gene expression database, such as NCBI’s Gene Expression Omnibus (GEO) and
Stanford Microarray Database (SMD), and develop their own model-based clustering


Spellman PT, Sherlock, G., Zhang, MQ, Iyer, VR, Anders, K., Eisen, MB, Brown, PO,
Botstein, D., and Futcher, B., (1998) Comprehensive identification of cell cycle-
regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol
Biol Cell 9(12):3273-97

Cho, R., Campbell MJ, Winzeler EA, Steinmetz L., Conway A., Wodicka L., Wolfsberg
TG, Gabrielian AE, Landsman D., Lockhart DJ, and Davis RW, (1998) A Genome-Wide
Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell, 2:65–73

Eisen MB, Spellman PT, Brown, PO, and Botstein, D. (1998) Cluster analysis and
display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863-

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 (1): 111-139.

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