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Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

Salem S, Ozcaglar C - BioData Min (2014)

Bottom Line: We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links.The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links.Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA.

ABSTRACT

Background: Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression.

Results: We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

No MeSH data available.


Related in: MedlinePlus

Impact of edge pruning on the percentage of frequent edge clusters. An edge cluster is frequent if it appears in at least 7 graphs. An α value of 0.5 and a β value 0.5 were used to generate the hybrid graph.
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Figure 6: Impact of edge pruning on the percentage of frequent edge clusters. An edge cluster is frequent if it appears in at least 7 graphs. An α value of 0.5 and a β value 0.5 were used to generate the hybrid graph.

Mentions: We have noticed that for a small edge frequency threshold (k), a large percentage of the reported edge clusters are not frequent in at least k coexpression networks. Figure 6 shows the effect of the edge frequency threshold on the percentage of frequent edge clusters. It is clear that as we increase the edge frequency threshold, the percentage of frequent edge clusters (frequent in at least 7 graphs) increases. As we shall see next, this has a direct impact on the running time of the algorithm.


Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

Salem S, Ozcaglar C - BioData Min (2014)

Impact of edge pruning on the percentage of frequent edge clusters. An edge cluster is frequent if it appears in at least 7 graphs. An α value of 0.5 and a β value 0.5 were used to generate the hybrid graph.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4151083&req=5

Figure 6: Impact of edge pruning on the percentage of frequent edge clusters. An edge cluster is frequent if it appears in at least 7 graphs. An α value of 0.5 and a β value 0.5 were used to generate the hybrid graph.
Mentions: We have noticed that for a small edge frequency threshold (k), a large percentage of the reported edge clusters are not frequent in at least k coexpression networks. Figure 6 shows the effect of the edge frequency threshold on the percentage of frequent edge clusters. It is clear that as we increase the edge frequency threshold, the percentage of frequent edge clusters (frequent in at least 7 graphs) increases. As we shall see next, this has a direct impact on the running time of the algorithm.

Bottom Line: We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links.The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links.Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA.

ABSTRACT

Background: Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression.

Results: We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

No MeSH data available.


Related in: MedlinePlus