ArticleCancer research2018
MVisAGe Identifies Concordant and Discordant Genomic Alterations of Driver Genes in Squamous Tumors.
Article in Cancer research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Genomic and transcriptomic landscape of human gastrointestinal stromal tumors.Nature communications · 2024Article
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- BIRC2-BIRC3 amplification: a potentially druggable feature of a subset of head and neck cancers in patients with Fanconi anemia.Scientific reports · 2022Article
- Genomic characterization of rare molecular subclasses of hepatocellular carcinoma.Communications biology · 2021Article
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Integrated analyses of multiple genomic datatypes are now common in cancer profiling studies. Such data present opportunities for numerous computational experiments, yet analytic pipelines are limited. Tools such as the cBioPortal and Regulome Explorer, although useful, are not easy to access programmatically or to implement locally. Here, we introduce the MVisAGe R package, which allows users to quantify gene-level associations between two genomic datatypes to investigate the effect of genomic alterations (e.g., DNA copy number changes on gene expression). Visualizing Pearson/Spearman correlation coefficients according to the genomic positions of the underlying genes provides a powerful yet novel tool for conducting exploratory analyses. We demonstrate its utility by analyzing three publicly available cancer datasets. Our approach highlights canonical oncogenes in chr11q13 that displayed the strongest associations between expression and copy number, including
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Registered trials
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