Data as provided

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Difference with the selected reference

Table 1: Statistics for individual replicates

Table 2: Statistics for conditions

Table 3: Statistics for comparison of means between conditions

Table 4: Statistics for repeatability

About

About SuperPlotsOfData

SuperPlots can display different sources of variability that are present in experimental data.

Both technical replicates (repeated measurements of the same biological sample) and biological replicates (repeated measurements of different biological samples) will contribute variability to experimental data. Classical plots do not distinguish between these sources of variability and may therefore incorrectly convey the precision of the measurement. A SuperPlot shows the data of all measurements and distuingishes between biological replicates. This data visualization strategy was first published in a preprint and is now published in a peer reviewed paper by Lord et al (2020). The SuperPlotsOfData Shiny app builds on the PlotsOfData app for plotting the raw data (instead of a summary) to improve transparency and interpretation. Summary statistics (mean, median) are available for the individual (biological) replicates. Replicates can be identified by color and/or the shape of symbols. The user has full control over the visibility of the raw data and statistics by adjustment of the transparency (alpha).

Contact

SuperPlotsOfData is created and maintained by Joachim Goedhart
Bug reports and feature requests can be communicated in several ways:

Source

Source code is available at github/JoachimGoedhart
Please cite the publication if you use the app: "SuperPlotsOfData – a web app for the transparent display and quantitative comparison of continuous data from different conditions" - doi: 10.1091/mbc.E20-09-0583


Credits

SuperPlotsOfData is an extension of the app PlotsOfData (DOI: 10.1371/journal.pbio.3000202) and implements innovations in data visualization and analysis from different sources:


The colorblind safe palettes were developed by Paul Tol and Masataka Okabe and Kei Ito.