ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2020
hubViz: A Novel Tool for Hub-centric Visualization.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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4 authors at 4 institutions in 2 countries.
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Abstract
Visualization algorithms have been widely used for intuitive interrogation of genomic data and popularly used tools include MDS, t-SNE, and UMAP. However, these algorithms are not tuned for the visualization of binary data and none of them consider the hubness of observations for the visualization. In order to address these limitations, here we propose hubViz, a novel tool for hub-centric visualization of binary data. We evaluated the performance of hubViz with its application to the gene expression data measured in multiple brain regions of rats exposed to cocaine, the single-cell RNA-seq data of peripheral blood mononuclear cells treated with interferon beta, and the literature mining data to investigate relationships among diseases. We further evaluated the performance of hubViz using simulation studies. We showed that hubViz provides effective visual inspection by locating the hub in the center and the contrasting elements in the opposite sides around the center. We believe that hubViz and its software can be powerful tools that can improve visualizations of various genomic data. The hubViz is implemented as an R package hubviz, which is publicly available at https://dongjunchung.github.io/hubviz/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.