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Visualizing set relationships: EVenn's comprehensive approach to Venn diagrams.

Mei YangTong ChenYong-Xin LiuLuqi Huang
Published in: iMeta (2024)
Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step-by-step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user-friendly tool for the in-depth exploration of multiomics data.
Keyphrases
  • electronic health record
  • big data
  • randomized controlled trial
  • single cell
  • data analysis
  • optical coherence tomography
  • machine learning
  • living cells
  • fluorescent probe
  • single molecule