ABSTRACT
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.
Subject(s)
Computational Biology , Oligonucleotide Array Sequence Analysis/statistics & numerical data , Bacillus subtilis/genetics , Bacillus subtilis/metabolism , Breast Neoplasms/genetics , Data Interpretation, Statistical , Female , Gene Expression , Humans , Methionine/metabolismABSTRACT
We compute the differential scattering cross sections for a dielectric helix whose dimensions are comparable to the incident radiation wavelength, using a discrete model and a multiple scattering development (low energy model). The numerical results are compared to experimental data on microwave scattering by a dielectric right-handed helix.
