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PlotTwist: A web app for plotting and annotating continuous data

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  • Joachim Goedhart

Abstract

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwistThis Community Page article describes PlotTwist, an open source app created with R/shiny to simplify state-of-the-art visualization and annotation of results from experiments that generate continuous data; the app runs locally or online, and no coding skills are needed to use it.

Suggested Citation

  • Joachim Goedhart, 2020. "PlotTwist: A web app for plotting and annotating continuous data," PLOS Biology, Public Library of Science, vol. 18(1), pages 1-10, January.
  • Handle: RePEc:plo:pbio00:3000581
    DOI: 10.1371/journal.pbio.3000581
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    References listed on IDEAS

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    1. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
    2. Tracey L Weissgerber & Natasa M Milic & Stacey J Winham & Vesna D Garovic, 2015. "Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm," PLOS Biology, Public Library of Science, vol. 13(4), pages 1-10, April.
    3. Marten Postma & Joachim Goedhart, 2019. "PlotsOfData—A web app for visualizing data together with their summaries," PLOS Biology, Public Library of Science, vol. 17(3), pages 1-8, March.
    4. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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    1. Sine Yaganoglu & Konstantinos Kalyviotis & Christina Vagena-Pantoula & Dörthe Jülich & Benjamin M. Gaub & Maaike Welling & Tatiana Lopes & Dariusz Lachowski & See Swee Tang & Armando Del Rio Hernandez, 2023. "Highly specific and non-invasive imaging of Piezo1-dependent activity across scales using GenEPi," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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