Evidence map›Paper›PMID 31061104›Full record

ArticlePlant physiology2019

MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation.

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

Abstract read
In one paragraph

Article in Plant physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

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  16. The zinc finger protein 3 ofFrontiers in plant science · 2023
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Magdalena M JulkowskaBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia magalena.julkowska@kaust.edu.sa.ORCID 0000-0002-4259-8296
Stephanie SaadeBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0003-3817-7234
Gaurav AgarwalComputer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0001-6353-0955
Ge GaoBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0001-5488-8648
Yveline PaillesBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0003-2283-2759
Mitchell MortonBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0001-8375-7954
Mariam AwliaBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0002-9372-0933
Mark TesterBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID 0000-0002-5085-8801

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

Data AnalysisMultivariate AnalysisCluster AnalysisComputational BiologyData MiningReproducibility of ResultsSoftware

Identifiers

PMID31061104
PMCPMC6752927

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.