Non-metric analysis of temporal patterns captured in microarray dataY-h. Taguchi1, Y. Oono2
firstname.lastname@example.org, Department of Physics, Chuo Universit; email@example.com, Department of Physics, UIUC
The gene activities in the transcriptional response of cell
cycle-synchronized human fibroblasts to serum [Lyer et al.
Science 283, 83-87 (1999)] is analyzed by our novel
nonmetric multidimensional scaling algorithm. Although the
analysis of the microarray data with the aid of principal
component analysis cannot clearly unravel the temproal order in
the data, our intrinsically nonlinear analysis method
unambiguously gives a ring-like arrangement of the genes, along
which the gene activity peak rotates in time in an orderly fashion.
Although our method is fully unsupervised, the obtained results
are comparable to the results that would be obtained by detailed Fourier
http://www.granular.com/MDS/fig_ISMB2003.pdf ). Thus, our
result emphasizes that data mining is intrinsically a nonlinear
analysis, and nonmetric MDS could be a powerful means.
Especially, our novel nMDS algorithm is maximally non-metric and
is designed for large scale data sets, so it could be a useful data
mining method for bioinformatics data as well as other biological
data sets such as ecological, phylogenetic, and biochemical ones.