MV app flies its flag to the challenging frontier of multivariate data analysis

Maria Papanatsiou

Research output: Contribution to journalArticlepeer-review


We live in an era where omic approaches are essential to decode scientific hypotheses. Indeed, technological advances have accelerated science, resulting in a plethora of insights. During the past decade, the plant science community has profited from using large and high-throughput phenotypic platforms to characterize multiple traits across time and environmental conditions, enabling the in-depth examination of genotype-to-phenotype interactions. However, such forward-moving experimental approaches require extensive data analysis, which can be very challenging. Are we ready to tackle this vast amount of phenomic data? Do we have adequate statistical literacy to properly interpret our results? And do we have standardized methods ensuring the accessibility, reproducibility, and transparency of our data?
Original languageEnglish (US)
Pages (from-to)1251-1252
Number of pages2
JournalPlant physiology
Issue number3
StatePublished - Jun 27 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-10
Acknowledgements: I thank Dr. Magdalena Julkowska for providing the illustration in Figure 1, which was produced by Ivan Gromicho, scientific illustrator at King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Plant Science
  • Genetics
  • Physiology


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