Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 11, Issue 6, Pages 1090-1109Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2004.11.1090
Keywords
gene expression data; blind source separation; independent component analysis; coregulated genes
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We develop an approach for the exploratory analysis of gene expression data, based upon blind source separation techniques. This approach exploits higher-order statistics to identify a linear model for ( logarithms of) expression profiles, described as linear combinations of independent sources. As a result, it yields elementary expression patterns ( the sources), which may be interpreted as potential regulation pathways. Further analysis of the so-obtained sources show that they are generally characterized by a small number of specific coexpressed or antiexpressed genes. In addition, the projections of the expression profiles onto the estimated sources often provides significant clustering of conditions. The algorithm relies on a large number of runs of independent component analysis with random initializations, followed by a search of consensus sources. It then provides estimates for independent sources, together with an assessment of their robustness. The results obtained on two datasets ( namely, breast cancer data and Bacillus subtilis sulfur metabolism data) show that some of the obtained gene families correspond to well known families of coregulated genes, which validates the proposed approach.
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