Construction and comparison of gene co-expression networks shows complex plant immune responses
Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species....
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2014-10-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/610.pdf |
| _version_ | 1797424364934660096 |
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| author | Luis Guillermo Leal Camilo López Liliana López-Kleine |
| author_facet | Luis Guillermo Leal Camilo López Liliana López-Kleine |
| author_sort | Luis Guillermo Leal |
| collection | DOAJ |
| description | Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses. |
| first_indexed | 2024-03-09T08:01:10Z |
| format | Article |
| id | doaj.art-ff4b6ce05c1745babde3100c0b1d3b13 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:01:10Z |
| publishDate | 2014-10-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-ff4b6ce05c1745babde3100c0b1d3b132023-12-03T00:47:28ZengPeerJ Inc.PeerJ2167-83592014-10-012e61010.7717/peerj.610610Construction and comparison of gene co-expression networks shows complex plant immune responsesLuis Guillermo Leal0Camilo López1Liliana López-Kleine2Department of Statistics, Universidad Nacional de Colombia, Bogotá, ColombiaDepartment of Biology, Universidad Nacional de Colombia, Bogotá, ColombiaDepartment of Statistics, Universidad Nacional de Colombia, Bogotá, ColombiaGene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.https://peerj.com/articles/610.pdfGene co-expression networksSimilarity measuresSimilarity thresholdPrincipal Component AnalysisNetworks comparisonPlant immunity |
| spellingShingle | Luis Guillermo Leal Camilo López Liliana López-Kleine Construction and comparison of gene co-expression networks shows complex plant immune responses PeerJ Gene co-expression networks Similarity measures Similarity threshold Principal Component Analysis Networks comparison Plant immunity |
| title | Construction and comparison of gene co-expression networks shows complex plant immune responses |
| title_full | Construction and comparison of gene co-expression networks shows complex plant immune responses |
| title_fullStr | Construction and comparison of gene co-expression networks shows complex plant immune responses |
| title_full_unstemmed | Construction and comparison of gene co-expression networks shows complex plant immune responses |
| title_short | Construction and comparison of gene co-expression networks shows complex plant immune responses |
| title_sort | construction and comparison of gene co expression networks shows complex plant immune responses |
| topic | Gene co-expression networks Similarity measures Similarity threshold Principal Component Analysis Networks comparison Plant immunity |
| url | https://peerj.com/articles/610.pdf |
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