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gene_networks_inference [2019/05/22 03:41] – [Data] admingene_networks_inference [2019/07/23 15:39] – [\\ Related software] admin
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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:** [[https://bitbucket.org/habilzare/genetwork/src/master/code/iNETgrate/|This]] R package is useful to integrate DNA methylation and gene expression data into //a single //network. 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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   - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://www.cell.com/cell/abstract/S0092-8674(17)30639-6?innerTabgraphical_S0092867417306396|Cell]], 2017 [{{:ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}}  ]. \\  TCGA's HCC data and subtyping using DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples.   - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://www.cell.com/cell/abstract/S0092-8674(17)30639-6?innerTabgraphical_S0092867417306396|Cell]], 2017 [{{:ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}}  ]. \\  TCGA's HCC data and subtyping using DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples.
   - 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"
  
- \\ **Related software**+===== Related software =====
  
   - Weighted Gene Co-expression Network Analysis ([[http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/|WGCNA]]) developed at UCLA. The page has links to some good introductory workshops.   - Weighted Gene Co-expression Network Analysis ([[http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/|WGCNA]]) developed at UCLA. The page has links to some good introductory workshops.