In order to assess the effectiveness of the proposed algorithm on extracting biological modules from multiple gene coexpression graphs, we performed an experimental evaluation of the algorithm using multiple human gene expression datasets.
As defined by Beisel and Storz (2010), these circuits combine multiple overlapping single-input modules to coordinate responses to multiple biological signals.
Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes.
The method was successfully applied to the microarray gene expression data of a model species - Saccharomyces cerevisiae - measured in multiple biological conditions, identifying a number of highly confident gene regulatory modules.
We supposed that miRNA synergy can be dissected into two types: (a) the intra-modular regulatory effect of a miRNA pair upon the same biological process, i.e. participating in the same module and (b) the inter-modular regulatory effect of a miRNA pair upon multiple biological processes, i.e. each member of the pair is involved in a different module.
Regardless, the numerous spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast.
The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast.
We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.
This illustrates the value of IWGCNA: it identifies a candidate gene pathway that is centrally located within the blue module network, i.e. it identifies genes influencing multiple biological pathways.
