Details

Autor(en) / Beteiligte

• Julkowska, Magdalena M
• Saade, Stephanie
• Agarwal, Gaurav
• Gao, Ge
• Pailles, Yveline
• Morton, Mitchell
• Awlia, Mariam
• Tester, Mark

Titel

MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation

Ist Teil von

• Plant physiology (Bethesda), 2019-07, Vol.180 (3), p.1261-1276

Ort / Verlag

United States: American Society of Plant Biologists

Erscheinungsjahr

2019

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 article 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.