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

14 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

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