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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

Volcano plots of differential gene expression showing −log10 of the P-values evaluated by DESeq as a function of the log2 fold change (shown in the [−6, 6] only, 99th percentile). The dots highlighted in red are the genes involving in each subnetwork produced by the eight methods.
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Related In: Results  -  Collection


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f1-cin-suppl.6-2014-015: Volcano plots of differential gene expression showing −log10 of the P-values evaluated by DESeq as a function of the log2 fold change (shown in the [−6, 6] only, 99th percentile). The dots highlighted in red are the genes involving in each subnetwork produced by the eight methods.

Mentions: We assess the quality of subnetworks output by the eight methods from two aspects: coverage of significant genes and network modularity. First, we prepared volcano plots with log2(fold change) versus −log10(P-values) for each method and highlighted the found genes in the eight subnetworks in red, as shown in Figure 1. We find that jActiveModules using Greedy Search (jAM. GR), BioNet, and NetBox cover most of the significant genes in their subnetworks, while excluding insignificant genes. In contrast, jActiveModules using Simulated Annealing (jAM. SA), ClustEx, and NetWalker cover a large number of genes regardless of their significance. DEGAS covers more upregulated genes, whereas OptDis covers more downregulated genes.


Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Volcano plots of differential gene expression showing −log10 of the P-values evaluated by DESeq as a function of the log2 fold change (shown in the [−6, 6] only, 99th percentile). The dots highlighted in red are the genes involving in each subnetwork produced by the eight methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.6-2014-015: Volcano plots of differential gene expression showing −log10 of the P-values evaluated by DESeq as a function of the log2 fold change (shown in the [−6, 6] only, 99th percentile). The dots highlighted in red are the genes involving in each subnetwork produced by the eight methods.
Mentions: We assess the quality of subnetworks output by the eight methods from two aspects: coverage of significant genes and network modularity. First, we prepared volcano plots with log2(fold change) versus −log10(P-values) for each method and highlighted the found genes in the eight subnetworks in red, as shown in Figure 1. We find that jActiveModules using Greedy Search (jAM. GR), BioNet, and NetBox cover most of the significant genes in their subnetworks, while excluding insignificant genes. In contrast, jActiveModules using Simulated Annealing (jAM. SA), ClustEx, and NetWalker cover a large number of genes regardless of their significance. DEGAS covers more upregulated genes, whereas OptDis covers more downregulated genes.

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