Metrics to estimate differential co-expression networks

Hdl Handle:
http://hdl.handle.net/11285/627958
Title:
Metrics to estimate differential co-expression networks
Authors:
Gonzalez-Valbuena, Elpidio-Emmanuel; Trevino, Victor ( 0000-0002-7472-9844 )
Issue Date:
2017-11-10
Publisher:
BioMed Central
Discipline:
Ciencias de la Salud / Health Sciences
Abstract:
Abstract Background Detecting the differences in gene expression data is important for understanding the underlying molecular mechanisms. Although the differentially expressed genes are a large component, differences in correlation are becoming an interesting approach to achieving deeper insights. However, diverse metrics have been used to detect differential correlation, making selection and use of a single metric difficult. In addition, available implementations are metric-specific, complicating their use in different contexts. Moreover, because the analyses in the literature have been performed on real data, there are uncertainties regarding the performance of metrics and procedures. Results In this work, we compare four novel and two previously proposed metrics to detect differential correlations. We generated well-controlled datasets into which differences in correlations were carefully introduced by controlled multivariate normal correlation networks and addition of noise. The comparisons were performed on three datasets derived from real tumor data. Our results show that metrics differ in their detection performance and computational time. No single metric was the best in all datasets, but trends show that three metrics are highly correlated and are very good candidates for real data analysis. In contrast, other metrics proposed in the literature seem to show low performance and different detections. Overall, our results suggest that metrics that do not filter correlations perform better. We also show an additional analysis of TCGA breast cancer subtypes. Conclusions We show a methodology to generate controlled datasets for the objective evaluation of differential correlation pipelines, and compare the performance of several metrics. We implemented in R a package called DifCoNet that can provide easy-to-use functions for differential correlation analyses.
Keywords:
Differential correlation; Networks; Data simulation
Additional Links:
https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0152-6
Type:
Artículo / Article
Appears in Collections:
Artículos de Revistas

Full metadata record

DC FieldValue Language
dc.contributor.authorGonzalez-Valbuena, Elpidio-Emmanuelen
dc.contributor.authorTrevino, Victoren
dc.date.accessioned2017-11-21T20:52:19Z-
dc.date.available2017-11-21T20:52:19Z-
dc.date.issued2017-11-10-
dc.identifier.issn1756-0381-
dc.identifier.doihttp://dx.doi.org/10.1186/s13040-017-0152-6-
dc.identifier.urihttp://hdl.handle.net/11285/627958-
dc.description.abstractAbstract Background Detecting the differences in gene expression data is important for understanding the underlying molecular mechanisms. Although the differentially expressed genes are a large component, differences in correlation are becoming an interesting approach to achieving deeper insights. However, diverse metrics have been used to detect differential correlation, making selection and use of a single metric difficult. In addition, available implementations are metric-specific, complicating their use in different contexts. Moreover, because the analyses in the literature have been performed on real data, there are uncertainties regarding the performance of metrics and procedures. Results In this work, we compare four novel and two previously proposed metrics to detect differential correlations. We generated well-controlled datasets into which differences in correlations were carefully introduced by controlled multivariate normal correlation networks and addition of noise. The comparisons were performed on three datasets derived from real tumor data. Our results show that metrics differ in their detection performance and computational time. No single metric was the best in all datasets, but trends show that three metrics are highly correlated and are very good candidates for real data analysis. In contrast, other metrics proposed in the literature seem to show low performance and different detections. Overall, our results suggest that metrics that do not filter correlations perform better. We also show an additional analysis of TCGA breast cancer subtypes. Conclusions We show a methodology to generate controlled datasets for the objective evaluation of differential correlation pipelines, and compare the performance of several metrics. We implemented in R a package called DifCoNet that can provide easy-to-use functions for differential correlation analyses.en
dc.language.isoengen
dc.publisherBioMed Centralen
dc.relation.urlhttps://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0152-6en
dc.rightsOpen Accessen
dc.titleMetrics to estimate differential co-expression networksen
dc.typeArtículo / Articleen
dc.contributor.departmentTecnologico de Monterreyen
dc.language.rfc3066en-
dc.rights.holderThe Author(s).-
dc.date.updated2017-11-12T05:42:09Z-
dc.subject.keywordDifferential correlationen
dc.subject.keywordNetworksen
dc.subject.keywordData simulationen
dc.subject.disciplineCiencias de la Salud / Health Sciencesen
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