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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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Related in: MedlinePlus

Comparison of expression fold changes (FC) of the hub genes and non-hub genes between different cancer stages in BRCA, against lists of genes identified by mutiscale hub analysis, where fc denotes expression fold change.The numeric labels on x-axis represent the ranges of α values defining the resolution levels of the hubs, “multiscale” represents intersection of hub genes across different scales, and “non.hub” represents the rest of genes.
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pcbi.1004574.g010: Comparison of expression fold changes (FC) of the hub genes and non-hub genes between different cancer stages in BRCA, against lists of genes identified by mutiscale hub analysis, where fc denotes expression fold change.The numeric labels on x-axis represent the ranges of α values defining the resolution levels of the hubs, “multiscale” represents intersection of hub genes across different scales, and “non.hub” represents the rest of genes.

Mentions: To verify biological significance of hub genes detected by MHA, we compared expression fold changes of network hubs between different cancer stages, to those of the non-hub genes. We first identified the hub genes at each scale, and then intersected the hub gene sets at the different scales to identify a more stringent hub set, denoted as “multiscale hubs”. We then evaluated the significance of the difference between the fold change distributions from each hub set and the corresponding non-hub genes using the Kolmogorov-Smirnov (KS) test. Fig 10 compares the distributions of expression fold changes in the two groups with respect to different stages of breast cancer. S6 Fig shows p-values from the KS test.


Multiscale Embedded Gene Co-expression Network Analysis.

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

Comparison of expression fold changes (FC) of the hub genes and non-hub genes between different cancer stages in BRCA, against lists of genes identified by mutiscale hub analysis, where fc denotes expression fold change.The numeric labels on x-axis represent the ranges of α values defining the resolution levels of the hubs, “multiscale” represents intersection of hub genes across different scales, and “non.hub” represents the rest of genes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004574.g010: Comparison of expression fold changes (FC) of the hub genes and non-hub genes between different cancer stages in BRCA, against lists of genes identified by mutiscale hub analysis, where fc denotes expression fold change.The numeric labels on x-axis represent the ranges of α values defining the resolution levels of the hubs, “multiscale” represents intersection of hub genes across different scales, and “non.hub” represents the rest of genes.
Mentions: To verify biological significance of hub genes detected by MHA, we compared expression fold changes of network hubs between different cancer stages, to those of the non-hub genes. We first identified the hub genes at each scale, and then intersected the hub gene sets at the different scales to identify a more stringent hub set, denoted as “multiscale hubs”. We then evaluated the significance of the difference between the fold change distributions from each hub set and the corresponding non-hub genes using the Kolmogorov-Smirnov (KS) test. Fig 10 compares the distributions of expression fold changes in the two groups with respect to different stages of breast cancer. S6 Fig shows p-values from the KS test.

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