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In addition, this work presents the first complete microarray time course study of gene expression utilizing loop design.
Evidence is obtained from the application of our approach to microarray time course experimental replicated data, and suggests that gene features, once identified, can be used for stabilization purposes relatively to the replicate variability.
This was also observed in the microarray time course following inoculation with the bacterial pathogen P. syringae, with the non-host pv.
The microarray time course gene expression data can be downloaded from.
We retrieved microarray time course datasets for human T cell-lines from GEO [ 19] and ArrayExpress [ 20] databases.
Our goal is to recover the latent time-evolving network of gene interactions from microarray time course.
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In the past ten years, several groups have completed microarray time courses examining various aspects of C. elegans development [8] [14].
Normalized enzyme levels were inferred from the microarray time courses described above.
In the work of Brady et al. [ 3], two developmental microarray time courses were generated by taking 12 or 13 successive transverse sections along an Arabidopsis root (GEO Accession: GSE8934).
In this paper, we introduce a new multivariate data analyzing technique, the correspondence analysis, to analyze the high dimensional microarray time-course data in case control design.
Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case control design in clinical investigations has not been proposed perhaps due to the increased complexity for the existing parametric or non-parametric approaches.
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