Metacoder: An R package for visualization and manipulation of community taxonomic diversity data
By Zachary S. L. Foster, Thomas J. Sharpton, Niklaus J. Grünwald. bioRxiv, 2016
697 citations recorded by the source corpus.
Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual. Note: This article was previously submitted as a pre-print: Zachary S. L. Foster, Thomas J. Sharpton, Niklaus J. Grünwald. 2016. Metacoder: An R package for manipulation and heat tree visualization of community taxonomic data from metabar-coding. BioRxiv 071019; doi: http://dx.doi.org/10.1101/071019.
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6eb1809fa522e413a69df9c7521dfac3ed46aeae
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Citations in the corpus
- KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations (2020, background): “Heat parse trees (HPTs), similarly to “heat trees” in biology (Foster et al., 2017), are heatmaps over parse trees (see the colored tree in Fig.”
- microViz: an R package for microbiome data visualization and statistics (2021, methodology): “Other packages in R, such as ggtree (Yu et al., 2017) or metacoder (Foster et al., 2017), can be used to make annotated cladograms similar to the microViz taxonomic association tree visualizations, but the microViz style has a few advantages for the purpose of reporting multivariable model results:…”