Clustering context-specific gene regulatory networks.
Gene regulatory networks (GRNs) learned from high throughput genomic data are often hard to visualize due to the large number of nodes and edges involved, rendering them difficult to appreciate. This becomes an important issue when modular structures are inherent in the inferred networks, such as in the recently proposed context-specific GRNs.(12) In this study, we investigate the application of graph clustering techniques to discern modularity in such highly complex graphs, focusing on context-specific GRNs. Identified modules are then associated with a subset of samples and the key pathways enriched in the module. Specifically, we study the use of Markov clustering and spectral clustering on cancer datasets to yield evidence on the possible association amongst different tumor types. Two sets of gene expression profiling data were analyzed to reveal context-specificity as well as modularity in genomic regulations.
Ramesh
Archana
A
School of Computing, Informatics & Decision Systems Engineering, Arizona State University, 699 S Mill Avenue, Tempe, AZ 85281, USA.
Trevino
Robert
R
VON Hoff
Daniel D
DD
Kim
Seungchan
S
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D006801:Humans; D008390:Markov Chains; D009369:Neoplasms / Q000235:genetics