Added reference of preprint

README.md

@@ -1,13 +1,13 @@
 # SuperPlotsOfData
 
 SuperPlotsofData uses modern data visualization strategies for transparent presentation of data. The key features are:
-* identification of replicas and their statistical summary (mean of median) by colorblind friendly colors
+* identification of replicas and their statistical summary (mean of median) by colorblind friendly colors and/or symbols
 * communicate experimental design (number of replicas, paired or non-paired)
 * enables raincloud plots for plotting the data and distribution
 * makes a qantitative comparison between conditions by calculating the effect size (and a p-value)
 
 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](https://arxiv.org/abs/1911.03509) and is now published in a peer reviewed paper by [Lord et al (2020)](https://doi.org/10.1083/jcb.202001064).
-The SuperPlotsOfData Shiny app combines the idea of SuperPlots with the philosophy of [PlotsOfData app](https://huygens.science.uva.nl/PlotsOfData/) which was developped for plotting the raw data (instead of a summary) to improves transparency and interpretation. Summary statistics (mean, median) are available for the individual (biological replicates). The user has full control over the visibility of the raw data and statistics by adjustment of the transparency (alpha).
+The SuperPlotsOfData Shiny app combines the idea of SuperPlots with the philosophy of [PlotsOfData app](https://huygens.science.uva.nl/PlotsOfData/) which was developped for plotting the raw data (instead of a summary) to improves transparency and interpretation. Summary statistics (mean, median) are available for the individual (biological replicates). The user has full control over the visibility of the raw data and statistics by adjustment of the transparency (alpha). More details are reported in [a preprint](https://doi.org/10.1101/2020.09.01.276881) (doi: 10.1101/2020.09.01.276881)
 
 
 ### Example output

about.html

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 <h3>About SuperPlotsOfData</h3>
 <p>SuperPlots can display different sources of variability that are present in experimental data.</p> 
 <p>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 <a href="https://arxiv.org/abs/1911.03509">preprint</a> and is now published in a peer reviewed paper by <a href="https://doi.org/10.1083/jcb.202001064">Lord et al (2020)</a>.
-The SuperPlotsOfData Shiny app builds on the <a href="https://huygens.science.uva.nl/PlotsOfData/">PlotsOfData app</a> for plotting the raw data (instead of a summary) to improves transparency and interpretation. Summary statistics (mean, median) are available for the individual (biological replicates). The user has full control over the visibility of the raw data and statistics by adjustment of the transparency (alpha).</br> </p>
+The SuperPlotsOfData Shiny app builds on the <a href="https://huygens.science.uva.nl/PlotsOfData/">PlotsOfData app</a> 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).</br> </p>
 
 <h4>Contact</h4>
-<p>SuperPlotsOfData is created and maintained by Joachim Goedhart and Marten Postma</br>
+<p>SuperPlotsOfData is created and maintained by Joachim Goedhart</br>
 Bug reports and feature requests can be communicated in several ways: 
 <ul>
 <li>Github: <a href="https://github.com/JoachimGoedhart/SuperPlotsOfData/issues">SuperPlotsOfData/issues</a></li>
@@ -24,6 +24,8 @@ <h4>Contact</h4>
 <h4>Source</h4>
 Source code is available at <a href="https://github.com/JoachimGoedhart/SuperPlotsOfData">github/JoachimGoedhart</a>
 </br>
+Please cite <a href="https://doi.org/10.1101/2020.09.01.276881">the preprint</a> if you use the app: "SuperPlotsOfData - a web app for the transparent display and quantitative comparison of continuous data from discrete conditions" - doi: <a href="https://doi.org/10.1101/2020.09.01.276881">10.1101/2020.09.01.276881</a></p>
+
 </br>
 
 </p>