About us

This App is a result of hard work of a KAUST team, originating from The Salt Lab lead by Prof. Mark Tester.

This app is meant to streamline the data analysis that is common in many biological studies - especially when screaning large populations such as diversity panels or comparing multiple mutant lines to wild type. Our background is plant biology - so you know where our bias is ;).

You can follow the MVApp news on twitter @MVApp007 .

The current release of MVApp is published in Plant Physiology:
Julkowska, Magdalena; Saade, Stephanie; Agarwal, Gaurav; Gao, Ge; Pailles, Yveline; Morton, Mitchell; Awlia, Mariam; Tester, Mark (2019): MVAPP – Multivariate analysis application for streamlined data analysis and curation. doi: 10.1104/pp.19.00235

If you wish to cite the app itself, please use the following:

Julkowska, M.M., Saade, S., Gao, G., Morton, M.J.L., Awlia, M., Tester, M.A., MVApp.pre-release_v2.0 mmjulkowska/MVApp: MVApp.pre-release_v2.0, DOI: 10.5281/zenodo.1067974

The example dataset is available here

If you have any problems / questions / suggestions how we can improve MVApp so that YOU can do your analysis smoother - or simply you would like to tell us how amazing the MVApp is - please contact mmj55@cornell.edu

About the MVApp

We are aiming streamline the analysis of experiments containing multiple phenotypical measurements of the same sample, but you can easily use the app even if you have one phenotype.

Using the MVApp is completely safe! The MVApp will not save any of your data on the server, ensuring that your rights to the uploaded data are perserved.

User guide is available HERE , our tutorial videos can be found HERE and you can leave the feedback HERE - thank YOU :)

Our App empowers you to easily perform:

1. Fitting the curves using simple functions (linear, quadratic, exponential and square root) as well as by fitting cubic and smoothed splines

2. Automatically detect the outliers based on all traits or single trait

3. Perform summary statistics on the data with / without the outliers

4. Automatically determine whether your data is normally distributed and the variances between your samples are equal

5. Examine your data for significant effects of the Genotype, Treatment or any other independent variable you wish

6. Examine the correlations for all traits as well as for subsets of your data and easily determine the correlations that are changing depending on the Genotype, Treatment or any other selected independent variable.

7. Perform PCA analysis, examine which traits are contributing significantly to the most informative PCs and retrieve the coordinates of your samples.

8. Perform Multidimentional Scaling to detect the patterns in your data based on the relationships between your samples

9. Cluster your samples based on the selected traits and perform cluster validation analysis.

10. Use Quantile Regression to explore how individual phenotypes contribute to traits of major interest in different quantile classes

Although our App is super cool and everything is now just ONE click away from you, please remember that the final output will depend on your data - as they say >> rubbish in - rubbish out << , but of course we hope your data is all glam, glitter and rainbows! Happy Data analysis!