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Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

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Identification of hubs at various scale (defined by α) groups in the breast cancer PFN.A) Plots of various internal validity indices used for selecting the optimal number of clusters to group α values. B) Barplot showing summarized scores from normalized ranks by internal validity indices from A). C) A heatmap of the pairwise Euclidean distances between any two vectors of the within-cluster connectivity (determined by Cw(V,A)) of all the nodes at the corresponding scales. The color bar on the top of heatmap represents the distinct scale clusters identified by MHA.
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pcbi.1004574.g014: Identification of hubs at various scale (defined by α) groups in the breast cancer PFN.A) Plots of various internal validity indices used for selecting the optimal number of clusters to group α values. B) Barplot showing summarized scores from normalized ranks by internal validity indices from A). C) A heatmap of the pairwise Euclidean distances between any two vectors of the within-cluster connectivity (determined by Cw(V,A)) of all the nodes at the corresponding scales. The color bar on the top of heatmap represents the distinct scale clusters identified by MHA.

Mentions: Fig 14 shows the grouping process in analyzing the BRCA PFN where 6 distinct scales were identified.


Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

Identification of hubs at various scale (defined by α) groups in the breast cancer PFN.A) Plots of various internal validity indices used for selecting the optimal number of clusters to group α values. B) Barplot showing summarized scores from normalized ranks by internal validity indices from A). C) A heatmap of the pairwise Euclidean distances between any two vectors of the within-cluster connectivity (determined by Cw(V,A)) of all the nodes at the corresponding scales. The color bar on the top of heatmap represents the distinct scale clusters identified by MHA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664553&req=5

pcbi.1004574.g014: Identification of hubs at various scale (defined by α) groups in the breast cancer PFN.A) Plots of various internal validity indices used for selecting the optimal number of clusters to group α values. B) Barplot showing summarized scores from normalized ranks by internal validity indices from A). C) A heatmap of the pairwise Euclidean distances between any two vectors of the within-cluster connectivity (determined by Cw(V,A)) of all the nodes at the corresponding scales. The color bar on the top of heatmap represents the distinct scale clusters identified by MHA.
Mentions: Fig 14 shows the grouping process in analyzing the BRCA PFN where 6 distinct scales were identified.

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Show MeSH
Related in: MedlinePlus