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Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus

Modularity of the eight subnetworks.
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f3-cin-suppl.6-2014-015: Modularity of the eight subnetworks.

Mentions: To examine modularity of the eight subnetworks, we used two different measures: Global Clustering Coefficient (GCC)9 and Cut-Based Ratio (CBR).10 GCC measures how close a subnetwork is to a completely connected graph. And CBR measures the degree to which a subnetwork consists of more edges between nodes within the subnetwork and fewer edges between nodes inside and outside the subnetwork. Both modularity scores were scaled to the interval [0, 1] by dividing by the maximum quantities (Fig. 3). We can see that the OptDis subnetwork has the highest GCC, probably because there are many small (3 to 5 genes) fully connected modules in the subnetwork. In contrast, the ClustEx subnetwork has the highest CBR, probably due to the hierarchical clustering step used before growing the subnetwork within the clusters. The subnetworks of jAM.GR and DEGAS have moderately high modularity scores; both methods search for subnetworks using greedy strategies.


Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Modularity of the eight subnetworks.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4256043&req=5

f3-cin-suppl.6-2014-015: Modularity of the eight subnetworks.
Mentions: To examine modularity of the eight subnetworks, we used two different measures: Global Clustering Coefficient (GCC)9 and Cut-Based Ratio (CBR).10 GCC measures how close a subnetwork is to a completely connected graph. And CBR measures the degree to which a subnetwork consists of more edges between nodes within the subnetwork and fewer edges between nodes inside and outside the subnetwork. Both modularity scores were scaled to the interval [0, 1] by dividing by the maximum quantities (Fig. 3). We can see that the OptDis subnetwork has the highest GCC, probably because there are many small (3 to 5 genes) fully connected modules in the subnetwork. In contrast, the ClustEx subnetwork has the highest CBR, probably due to the hierarchical clustering step used before growing the subnetwork within the clusters. The subnetworks of jAM.GR and DEGAS have moderately high modularity scores; both methods search for subnetworks using greedy strategies.

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus