10.6084/m9.figshare.6291461.v2 Magdalena Julkowska Magdalena Julkowska Stephanie Saade Stephanie Saade Gaurav Agarwal Gaurav Agarwal Ge Gao Ge Gao Yveline Pailles Yveline Pailles Mitchell Morton Mitchell Morton Mariam Awlia Mariam Awlia Mark Tester Mark Tester MVAPP – Multivariate analysis application for streamlined data analysis and curation figshare 2019 Multivariate Analysis MVApp Data analysis outlier removal normal distribution equal variance curve fitting descriptive modeling polynomial curves ANOVA two-way ANOVA t-test correlation analyses Principal component analysis Multidimensional scaling quantile regression k-means clustering hierarchical clustering dimensionality reduction broad-sense heritability data analysis transparency Bioinformatics Time-Series Analysis Agricultural Systems Analysis and Modelling 2019-05-22 06:33:49 Journal contribution https://figshare.com/articles/journal_contribution/MVAPP_Multivariate_analysis_application_for_streamlined_data_analysis_and_curation/6291461 The revised and peer-reviewed version of this paper was published Open Access in Plant Physiology on May 2019 - please cite > DOI: https://doi.org/10.1104/pp.19.00235<br><br>Modern phenotyping enables the measurement of many phenotypic traits simultaneously, yielding vast amounts of quantitative data that is hard to manage and analyze. This type of data, when adequately examined, could reveal genotype-to-phenotype relationships and meaningful relationships between individual measured traits. Efficient data mining is currently challenging for experimental biologists, as many researchers are limited by their ability to curate, integrate and explore these complex outputs. Additionally, data transparency, accessibility and reproducibility have become important considerations for scientific publication. Thus, the need for a streamlined pipeline for curating phenotypic data is now more pressing than in the past. To address that need, we developed an open-source online platform for multivariate analysis, MVApp, which allows interactive data curation, in-depth data analysis and customized visualization. MVApp was developed in R using the Shiny framework, combining several functional R modules into a comprehensive toolkit that can be used without any prior knowledge of R, programming or advanced statistics. MVApp aims to enhance data transparency, standardize and facilitate phenotypic data curation and increase statistical literacy among the scientific community. Given that contributions from users in this open-source environment is encouraged, MVApp can be continuously updated and expanded to facilitate the analysis of high-throughput phenotyping outputs.