MVApp - Multivariate analysis application for streamlined data analysis and curation
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ArticleAuthors
Julkowska, MagdalenaSaade, Stephanie
Agarwal, Gaurav
Gao, Ge
Pailles, Yveline
Morton, Mitchell J L
Awlia, Mariam Sahal Abdulaziz
Tester, Mark A.

KAUST Department
Biological and Environmental Science and Engineering (BESE) DivisionBioscience Program
Center for Desert Agriculture
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Plant Science
Statistics Program
The Salt Lab
KAUST Grant Number
2302Date
2019-05-06Permanent link to this record
http://hdl.handle.net/10754/652855
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Show full item recordAbstract
Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating and exploring complex datasets. Additionally, data transparency, accessibility and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this manuscript we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.Citation
Julkowska MM, Saade S, Agarwal G, Gao G, Pailles Y, et al. (2019) MVApp - Multivariate analysis application for streamlined data analysis and curation. Plant Physiology: pp.00235.2019. Available: http://dx.doi.org/10.1104/pp.19.00235.Sponsors
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), through both baseline support to MT and under Office of Sponsored Research (OSR) Award No. 2302. Figure 9 was produced by Ivan Gromicho, scientific illustrator at KAUST. We would like to thank Antonio Arena from Research Computing at King Abdullah University of Science and Technology (KAUST) for his help with putting MVApp on the server and making it accessible online; KAUST IT Linux Systems Team who provided the infrastructure for the online hosting of MVApp; and Veronica Tremblay, scientific editor at KAUST, for editing the manuscript. Additionally, we would like to thank Dr. Guillaume Lobet (Louvain / Jurlich University), Dr. Sandra Schmöckel and Dr. Boubacar Kountche (KAUST), Prof. Julia Bailey-Serrez (UC Riverside) and Dr. Nazgol Emrani (Kiel University) for their helpful comments on the MVApp design and functionality.Journal
Plant PhysiologyAdditional Links
http://www.plantphysiol.org/content/early/2019/05/06/pp.19.00235ae974a485f413a2113503eed53cd6c53
10.1104/pp.19.00235