In this way, we used this approach to identify and visualize modules of coexpressed genes, which were organized into modules of coexpressed genes with clear functional interpretations, and to explore module differences between breeds.
To further search for evidence of the coordinated regulation of each identified module, we explored the module eigengene in tumor and control samples for all five modules.
The axial and rhombic components of the alignment tensor were explored with MODULE 1.0 and a grid search procedure to obtain reliable values of Da and R. The grid search was repeated using RDC values from the SLIds2 stem (residues 1 6, 16 21) and the SLV stem (residues 2 7, 15 20), independently.
Then, promoter regions in human genome were explored for modules and 328 genes containing the same modules were detected.
Tests have been performed to explore the module performance under various operating conditions including detector bias voltage, magnetic field, incidence angle, and state of irradiation up to 3×1014 protons per square centimetre.
Combining these options with the other filtering options provides a quick and powerful way to explore the module.
Four modules were presented below to illustrate the correspondence of these modules with defined biological functions and methods that can be used to explore functional modules from these gene coexpression networks.
Specifically, this module explored the role of culture and health and presented evidence that the experience of illness and perception of disease varies by culture and that culture influences help seeking and health care utilization.
This integrative approach allows to systematically explore functional modules in the network and it is highly complementary to existing bi-clustering and other module identification methods.
From bottom up after the property of each module is conveniently explored, a hierarchy of modules of increasing size may be built until the whole network is covered.
