MVApp - Multivariate analysis application for streamlined data analysis and curation
Morton, Mitchell J L
Awlia, Mariam Sahal Abdulaziz
Tester, Mark A.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Desert Agriculture Initiative
KAUST Grant Number2302
Permanent link to this recordhttp://hdl.handle.net/10754/652855
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AbstractModern 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.
CitationJulkowska 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.
SponsorsThe 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.