[HTML][HTML] VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots

J Goedhart, MS Luijsterburg - Scientific reports, 2020 - nature.com
Scientific reports, 2020nature.com
Comparative genome-and proteome-wide screens yield large amounts of data. To efficiently
present such datasets and to simplify the identification of hits, the results are often presented
in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus
a measure of significance. The data points with the largest effect size and a statistical
significance beyond a user-defined threshold are considered as hits. Such hits are usually
annotated in the plot by a label with their name. Volcano plots can represent ten thousands …
Abstract
Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/Shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.
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