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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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Kaplan-Meier plots of subgroups separated by median expressions of two hub genes AQP7 (A) and CIDEC (B), showing significant logrank p-values.Blue curves showing lower risks correspond to lower expressions, and red curves showing higher risks correspond to higher expressions.
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pcbi.1004574.g008: Kaplan-Meier plots of subgroups separated by median expressions of two hub genes AQP7 (A) and CIDEC (B), showing significant logrank p-values.Blue curves showing lower risks correspond to lower expressions, and red curves showing higher risks correspond to higher expressions.

Mentions: Among these MEGENA-specific FACs, one cluster (comp1_56) is enriched for the genes in adipocytokine signaling pathway (corrected FET p = 0.02, 2.7 fold). The hub genes of this cluster are all significantly associated with the overall survival of the BRCA Luminal-B patients. Luminal-B, a molecular subtype of hormone-receptor positive breast cancers, is associated with higher grade and increased proliferation rate, and has a poorer overall prognosis than its hormone-receptor positive counterpart, Luminal-A [41]. Fig 7A and 7B show the localization of genes with univariate Cox p-value < 0.05 for the Luminal B patients’ overall survival at the adipocytokine-enriched cluster. The hub genes of this cluster are all predictive of the Luminal B patients’ overall survival: AQP7 (Cox p-value < 1.5e-3), C14orf180 (Cox p-value < 4.4e-3), CIDEC (Cox p-value < 1.6e-3), CIDEA (Cox p-value < 2.1e-2) and MRAP (Cox p-value < 2.2e-2). We further examined the significance of the survival difference between expression median defined subgroups for each hub gene. Fig 8 shows the Kaplan-Meier plots for the two most predictive hub genes, AQP7 and CIDEC.


Multiscale Embedded Gene Co-expression Network Analysis.

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

Kaplan-Meier plots of subgroups separated by median expressions of two hub genes AQP7 (A) and CIDEC (B), showing significant logrank p-values.Blue curves showing lower risks correspond to lower expressions, and red curves showing higher risks correspond to higher expressions.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004574.g008: Kaplan-Meier plots of subgroups separated by median expressions of two hub genes AQP7 (A) and CIDEC (B), showing significant logrank p-values.Blue curves showing lower risks correspond to lower expressions, and red curves showing higher risks correspond to higher expressions.
Mentions: Among these MEGENA-specific FACs, one cluster (comp1_56) is enriched for the genes in adipocytokine signaling pathway (corrected FET p = 0.02, 2.7 fold). The hub genes of this cluster are all significantly associated with the overall survival of the BRCA Luminal-B patients. Luminal-B, a molecular subtype of hormone-receptor positive breast cancers, is associated with higher grade and increased proliferation rate, and has a poorer overall prognosis than its hormone-receptor positive counterpart, Luminal-A [41]. Fig 7A and 7B show the localization of genes with univariate Cox p-value < 0.05 for the Luminal B patients’ overall survival at the adipocytokine-enriched cluster. The hub genes of this cluster are all predictive of the Luminal B patients’ overall survival: AQP7 (Cox p-value < 1.5e-3), C14orf180 (Cox p-value < 4.4e-3), CIDEC (Cox p-value < 1.6e-3), CIDEA (Cox p-value < 2.1e-2) and MRAP (Cox p-value < 2.2e-2). We further examined the significance of the survival difference between expression median defined subgroups for each hub gene. Fig 8 shows the Kaplan-Meier plots for the two most predictive hub genes, AQP7 and CIDEC.

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