Identification of highly synchronized subnetworks from gene expression data

Abstract

BackgroundThere has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.MethodsHere we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.ResultsApplication of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.ConclusionsInclusion of interaction dynamics is important to the identification of relevant gene networks.

Advanced Applications of RNA Sequencing and Challenges

“…147 The current approach of building dynamic network biomarker is based on population data. It might be interesting to build a co-expression of network with time series data from same subject with self-correlation or synchronization, 148,149 such that we can use it to predict disease onset for diagnosis and personalized medicine.…”

Comparing host module activation patterns and temporal dynamics in infection by influenza H1N1 viruses

“…While the majority of integrative gene expression and interaction network analyses have not utilized the temporal dimension of the data, there have been attempts to incorporate temporal information into module discovery (19,20,21,22,23,24). For example, Gao and Wang (22) used a phase-locking approach (25) to identify yeast cell cycle genes that show temporal coordination and whose interactions are supported by a PPI network. In another study, Jin and colleagues (23) applied a time-warping dynamic programming algorithm (26) to identify locally-similar temporal expression patterns among groups of genes forming connected components of a PPI network.…”

“…Time-course gene expression datasets capture important features of the temporal trajectories of transcriptional changes. While the majority of integrative gene expression and interaction network analyses have not utilized the temporal dimension of the data, there have been attempts to incorporate temporal information into module discovery (19,20,21,22,23,24). For example, Gao and Wang (22) used a phase-locking approach (25) to identify yeast cell cycle genes that show temporal coordination and whose interactions are supported by a PPI network.…”

Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses

“…Time course gene expression datasets capture important features of the temporal trajectories of transcriptional changes. While the majority of integrative gene expression and interaction network analyses have not utilized the temporal dimension of the data, there have been attempts to incorporate temporal information into module discovery (19)(20)(21)(22)(23)(24). For example, Gao and Wang (22) used a phase-locking approach (25) to identify yeast cell cycle genes that show temporal coordination and whose interactions are supported by a PPI network.…”