A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma.
Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1005739 |
| _version_ | 1819259663762325504 |
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| author | André Voigt Katja Nowick Eivind Almaas |
| author_facet | André Voigt Katja Nowick Eivind Almaas |
| author_sort | André Voigt |
| collection | DOAJ |
| description | Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations. |
| first_indexed | 2024-12-23T19:13:36Z |
| format | Article |
| id | doaj.art-af933df656ae4f1184eb024d48b9f20c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-23T19:13:36Z |
| publishDate | 2017-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-af933df656ae4f1184eb024d48b9f20c2022-12-21T17:34:23ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-09-01139e100573910.1371/journal.pcbi.1005739A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma.André VoigtKatja NowickEivind AlmaasDifferential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.https://doi.org/10.1371/journal.pcbi.1005739 |
| spellingShingle | André Voigt Katja Nowick Eivind Almaas A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. PLoS Computational Biology |
| title | A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. |
| title_full | A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. |
| title_fullStr | A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. |
| title_full_unstemmed | A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. |
| title_short | A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. |
| title_sort | composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma |
| url | https://doi.org/10.1371/journal.pcbi.1005739 |
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