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gene_networks_inference [2019/08/16 00:09] – [Data] admingene_networks_inference [2020/01/06 16:30] – [Software] 107.1.85.154
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 **Pigengene:** This R package provides an efficient way to perform network analysis and to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., it can infer the signatures using data from microarray and evaluate them in an independent RNA Seq dataset. It is approved by, and publicly available from, [[https://bioconductor.org/packages/Pigengene|Bioconductor]]. **Pigengene:** This R package provides an efficient way to perform network analysis and to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., it can infer the signatures using data from microarray and evaluate them in an independent RNA Seq dataset. It is approved by, and publicly available from, [[https://bioconductor.org/packages/Pigengene|Bioconductor]].
  
-**iNETgrate:** This R package is useful to integrate DNA methylation and gene expression data into //a single //network [[[https://bitbucket.org/habilzare/genetwork/src/master/|code]]]. This approach leads to identification of more robust gene modules compared to conventional coexpression networks. The package will be publicly available after review and approval by Bioconductor.+**iNETgrate:** This R package is useful to integrate DNA methylation and gene expression data into //a single //network [[[:https:bitbucket.org_habilzare_genetwork_src_master|code]]]. This approach leads to identification of more robust gene modules compared to conventional coexpression networks. The package will be publicly available after review and approval by Bioconductor.
  
  
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   - Guillamot, Maria, Luisa Cimmino, and Iannis Aifantis. "The impact of DNA methylation in hematopoietic malignancies." [[http://www.cell.com/trends/cancer/pdf/S2405-8033(15)00089-8.pdf|Trends in cancer]] 2.2 (2016): 70-83. \\  Reviews and references DNA methylation studies and datasets on AML. E.g., [[http://www.sciencedirect.com/science/article/pii/S1535610809004206|Figueroa]] et al. used DNA methylation for classification of 344 AML cases. [[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002781|Akalin]] et al. related DNA methylation patterns with mutations in 5 AML cases. "The methylation status of specific genes can predict the future survival of AML patients, suggesting that DNA methylation is a biomarker for clinical outcome" see e.g., Figueroa et al, [[http://www.bloodjournal.org/content/bloodjournal/113/6/1315.full.pdf?sso-checked=true|Jiang]] 2009 (studied MDS to AML progression in 184 cases), and [[http://www.bloodjournal.org/content/115/3/636.long?sso-checked=true|Bullinger]] 2010 (analyzed 92 genomic regions in 182 patients).   - Guillamot, Maria, Luisa Cimmino, and Iannis Aifantis. "The impact of DNA methylation in hematopoietic malignancies." [[http://www.cell.com/trends/cancer/pdf/S2405-8033(15)00089-8.pdf|Trends in cancer]] 2.2 (2016): 70-83. \\  Reviews and references DNA methylation studies and datasets on AML. E.g., [[http://www.sciencedirect.com/science/article/pii/S1535610809004206|Figueroa]] et al. used DNA methylation for classification of 344 AML cases. [[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002781|Akalin]] et al. related DNA methylation patterns with mutations in 5 AML cases. "The methylation status of specific genes can predict the future survival of AML patients, suggesting that DNA methylation is a biomarker for clinical outcome" see e.g., Figueroa et al, [[http://www.bloodjournal.org/content/bloodjournal/113/6/1315.full.pdf?sso-checked=true|Jiang]] 2009 (studied MDS to AML progression in 184 cases), and [[http://www.bloodjournal.org/content/115/3/636.long?sso-checked=true|Bullinger]] 2010 (analyzed 92 genomic regions in 182 patients).
   - John [[https://www.youtube.com/watch?v=Vyhq7GZFnes|Quackenbush's]] talk entitled: "Using Networks to Understand the Genotype-Phenotype Connection".   - John [[https://www.youtube.com/watch?v=Vyhq7GZFnes|Quackenbush's]] talk entitled: "Using Networks to Understand the Genotype-Phenotype Connection".
-  - Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. "A comprehensive evaluation of module detection methods for gene expression data." [[https://www.nature.com/articles/s41467-018-03424-4|Nature communications]] 9.1 (2018): 1090.  \\  "Graph-based, representative-based, and hierarchical clustering all performed equally well, with the clustering method FLAME (Fuzzy clustering by Local Approximation of Memberships), one of the only clustering methods able to detect overlap, slightly outperforming other clustering methods" including WGCNA. Regularity networks that had been inferred using other data, e.g., "binding motifs in active enhancers", were used as gold standard.+  - Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. "A comprehensive evaluation of module detection methods for gene expression data." [[https://www.nature.com/articles/s41467-018-03424-4|Nature communications]] 9.1 (2018): 1090. \\  "Graph-based, representative-based, and hierarchical clustering all performed equally well, with the clustering method FLAME (Fuzzy clustering by Local Approximation of Memberships), one of the only clustering methods able to detect overlap, slightly outperforming other clustering methods" including WGCNA. Regularity networks that had been inferred using other data, e.g., "binding motifs in active enhancers", were used as gold standard. 
 +  - Choobdar, Sarvenaz, et al. "Assessment of network module identification across complex diseases." [[https://www.nature.com/articles/s41592-019-0509-5|Nature Methods]] 16.9 (2019): 843-852. \\  "The popular weighted gene co-expression network analysis (WGCNA) method7 did not perform competitively."