Correspondence analysis applied to microarray data
Proceedings of the National Academy of Sciences, 98 (19):
10781--10786 (2001)DOI: 10.1073/pnas.181597298
Abstract
Correspondence analysis is an explorative computational method for
the study of associations between variables. Much like principal
component analysis, it displays a low-dimensional projection of the
data, e.g., into a plane. It does this, though, for two variables
simultaneously, thus revealing associations between them. Here, we
demonstrate the applicability of correspondence analysis to and high
value for the analysis of microarray data, displaying associations
between genes and experiments. To introduce the method, we show its
application to the well-known Saccharomyces cerevisiae cell-cycle
synchronization data by Spellman et al. Spellman, P. T., Sherlock,
G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P.
O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273-3297,
allowing for comparison with their visualization of this data set.
Furthermore, we apply correspondence analysis to a non-time-series
data set of our own, thus supporting its general applicability to
microarray data of different complexity, underlying structure, and
experimental strategy (both two-channel fluorescence-tag and radioactive
labeling).
