Gene arrays data analysis

Oxford Gene Technologies offers services and licenses their proprietary technologies. The array technique surveys hybridization across gene sequences. The customized DNA microarray service supports research activities, and includes consultation, experimental design, data analysis, and interpretation. 

Cross comparisons across toxicogenomic datasets provides new statistical questions and subsequent challenges in data analysis. Meta-analyses may integrate datasets from multiple experimental studies consisting of different models (species, source), platforms (array type), statistical techniques (normalization) and design. The first challenge that needs to be addressed is how to properly make comparisons across datasets. To normalize datasets, better results may be achieved when data is first normalized internally and then externally (88). Secondly, equivalent and current annotation is needed to identify common genes across platforms, models, etc. 

Mateos A, Herrero J, Tamames J, Dopazo J, Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, In Lin SM, Johnson KF, eds., Methods of Microarray Data Analysis II, Boston, Kluwer Academic Publ, pp. 91-103, 2002. 

This section describes the use of the microarray technology for transcriptional profiling. The section includes a summary of the experimental procedures (section on Experimental design), the most widely used array platforms (section on Platforms for Gene Expression Analysis), and briefly summarizes the data analysis steps, the software that can be used, and the public microarray data repositories (sections on Analysis of microarray data and Data sharing). 

Chen et al. (130) proposed the theoretical basis for a series of differential equation-based models for gene expression in the context of array analysis but, surprisingly, did not explore specific applications of the differential equation-based approach to array data sets. 

---