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.