MVApp - Multivariate analysis application for streamlined data analysis and curation

Magdalena Julkowska, Stephanie Saade, Gaurav Agarwal, Ge Gao, Yveline Pailles, Mitchell J L Morton, Mariam Sahal Abdulaziz Awlia, Mark A. Tester

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)1261-1276
Number of pages16
JournalPlant Physiology
Volume180
Issue number3
DOIs
StatePublished - May 6 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): 2302
Acknowledgements: 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.

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