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gene_networks_inference [2019/06/03 19:16]
admin [Software]
gene_networks_inference [2020/05/27 06:00] (current)
admin [iNETgrate]
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 ====== Gene networks inference ====== ====== Gene networks inference ======
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 ===== Objectives ===== ===== Objectives =====
 Biological processes in a cell often require intricate coordination between //​multiple//​ genes and proteins. The goal of this project is to infer useful biological and clinical information from large networks of thousands of genes. We develop an integrative approach to analyze co-expression and DNA methylation patterns in a single model. The results will be useful in pinpointing the cause and mechanism of complex diseases such as cancer. Our findings can potentially open doors to new targets for novel treatment plans. \\ Biological processes in a cell often require intricate coordination between //​multiple//​ genes and proteins. The goal of this project is to infer useful biological and clinical information from large networks of thousands of genes. We develop an integrative approach to analyze co-expression and DNA methylation patterns in a single model. The results will be useful in pinpointing the cause and mechanism of complex diseases such as cancer. Our findings can potentially open doors to new targets for novel treatment plans. \\
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 ===== Software ===== ===== Software =====
  
-**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 ​====
  
-**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.+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]]. 
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 +==== iNETgrate ==== 
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 +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/​Ghazal/​iNETgrate/|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. A [[https://​docs.google.com/​document/​d/​17ZlnpNFm2QD58j4AI-GbyW_9TPXT_e5dsGYm_DPV2Fs/​edit|checklist]] for package completion tasks.
  
  
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   - The [[https://​www.nature.com/​articles/​s41586-018-0623-z#​Sec38|BEAT]] ALM dataset of ~300 cases including gene expression, survival, ELN17, etc.   - The [[https://​www.nature.com/​articles/​s41586-018-0623-z#​Sec38|BEAT]] ALM dataset of ~300 cases including gene expression, survival, ELN17, etc.
   - [[https://​www.leukemiaatlas.org/​adultaml|Leukemia Protein Atlas]]: Expression of hundreds of proteins were measured in bone marrow and PB samples of ~200 AML cases. A good publicly available resource to validate findings based on gene expression assays.   - [[https://​www.leukemiaatlas.org/​adultaml|Leukemia Protein Atlas]]: Expression of hundreds of proteins were measured in bone marrow and PB samples of ~200 AML cases. A good publicly available resource to validate findings based on gene expression assays.
 +  - ~40K [[https://​www.cell.com/​cell/​fulltext/​S0092-8674(19)30094-7?​_returnURL=https://​linkinghub.elsevier.com/​retrieve/​pii/​S0092867419300947?​showall=true|single cell]] RNA-Seq data from 40 bone marrow aspirates, including 16 AML patients and \\  5 healthy donors.
  
  
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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"​
 +  - 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."​ 
  
- \\ **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.