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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

Frequent edge clusters vs. topological similarity contribution. The Percentage of edge clusters that appear in at least N graphs is shown for varying α thresholds. The hybrid similarity threshold, β, was set to 0.4.
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Figure 3: Frequent edge clusters vs. topological similarity contribution. The Percentage of edge clusters that appear in at least N graphs is shown for varying α thresholds. The hybrid similarity threshold, β, was set to 0.4.

Mentions: To investigate the effect of the contribution of topological similarity on the characteristics of the reported edge clusters, we fixed β to 0.4 and ran the algorithm for varying α thresholds. Figure 2 shows the percentage of γ-approximate edge clusters that appear in at least 5 graphs for different α thresholds. Recall that for γ = 1, all the edges of an edge cluster must appear in a given graph to count as an occurrence. The highest percentage of edge clusters is achieved for almost balanced contribution of topological and attribute similarity. Figure 3 shows the percentage of the frequent edge clusters that appear in at least N graphs. For α values of 0.6 and 0.7, higher percentages of the edge clusters are frequent in at least N graphs.


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

Salem S, Ozcaglar C - BioData Min (2014)

Frequent edge clusters vs. topological similarity contribution. The Percentage of edge clusters that appear in at least N graphs is shown for varying α thresholds. The hybrid similarity threshold, β, was set to 0.4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Frequent edge clusters vs. topological similarity contribution. The Percentage of edge clusters that appear in at least N graphs is shown for varying α thresholds. The hybrid similarity threshold, β, was set to 0.4.
Mentions: To investigate the effect of the contribution of topological similarity on the characteristics of the reported edge clusters, we fixed β to 0.4 and ran the algorithm for varying α thresholds. Figure 2 shows the percentage of γ-approximate edge clusters that appear in at least 5 graphs for different α thresholds. Recall that for γ = 1, all the edges of an edge cluster must appear in a given graph to count as an occurrence. The highest percentage of edge clusters is achieved for almost balanced contribution of topological and attribute similarity. Figure 3 shows the percentage of the frequent edge clusters that appear in at least N graphs. For α values of 0.6 and 0.7, higher percentages of the edge clusters are frequent in at least N graphs.

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