ArticleNucleic acids research2015
Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.Biometrics · 2020Article
- NExUS: Bayesian simultaneous network estimation across unequal sample sizes.Bioinformatics (Oxford, England) · 2020Article
- Integrative discovery of treatments for high-risk neuroblastoma.Nature communications · 2020Article
- Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.Bioinformatics (Oxford, England) · 2019Article
- A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data.Briefings in bioinformatics · 2019Review
- Increasing the accuracy of glioblastoma subtypes: Factoring in the tumor's cell of origin.Molecular & cellular oncology · 2019Article
- Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.Scientific reports · 2018Article
- Review
- Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma.EBioMedicine · 2016Article
- GAM: a web-service for integrated transcriptional and metabolic network analysis.Nucleic acids research · 2016Article
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Authors and funding
6 authors.
Funding
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Abstract
Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.
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