<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next
to a neutral control, examining relative changes (ratios). Independent component
analysis (ICA) is a promising modern method for the analysis of such experiments.
The condition of ICA algorithms can, however, depend on the characteristics
of the data examined, making algorithm properties such as robustness specific
to the given application domain. To address the lack of studies examining the
robustness of ICA applied to microarray measurements, we report on the stability of
variational Bayesian ICA in this domain. Microarray data are usually preprocessed
and transformed. Hence we first examined alternative transforms and data selections
for the smallest modelling reconstruction errors. Log-ratio data are reconstructed
better than non-transformed ratio data by our linear model with a Gaussian error
term. To compare ICA results we must allow for ICA invariance under rescaling
and permutation of the extracted <italic>signatures</italic>, which hold the loadings of the original
variables (gene transcript ratios) on particular latent variables. We introduced a
method to optimally match corresponding signatures between sets of results. The
stability of signatures was then examined after (1) repetition of the same analysis run
with different random number generator seeds, and (2) repetition of the analysis
with partial data sets. The effects of both dropping a proportion of the gene
transcript ratios and dropping measurements for several samples have been studied.
In summary, signatures with a high relative data power were very likely to be
retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wildtype
vs. wild-type experiments, moreover, yielded 10 reliably identified signatures,
demonstrating that the variance observed is not just noise.</p>
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Comp Funct Genomics. 2003 Jun; 4(3):300-317