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Related Concept Videos

Scatter Plot01:15

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Modified Boxplots00:57

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Updated: Sep 18, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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DicePlot: a package for high-dimensional categorical data visualization.

Matthias Flotho1,2, Philipp Flotho1, Andreas Keller1,2

  • 1Clinical Bioinformatics, Center for Bioinformatics, Saarland University, Saarland University Campus, 66123 Saarland, Germany.

Bioinformatics (Oxford, England)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

New DicePlots and DominoPlots offer intuitive visualizations for complex, multidimensional categorical data, aiding in life sciences research like gene and pathway analysis.

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Data Visualization

Background:

  • Multidimensional, categorical data visualization is a significant challenge in scientific research.
  • Effective visualization is crucial for comprehensive data overview and multi-variable assessment.
  • Gene and pathway analysis require tools to identify dysregulation across multiple conditions.

Purpose of the Study:

  • To introduce novel visualization techniques for multidimensional categorical data.
  • To provide an intuitive representation for complex biological data.
  • To enhance the assessment of multiple variables in scientific domains.

Main Methods:

  • Development of DicePlots to visualize up to four distinct categorical classes.
  • Implementation of DominoPlots for additional binary comparison insights.
  • Creation of the diceplot R package and pydiceplot Python package for accessibility.

Main Results:

  • DicePlots provide a single view for up to four categorical variables.
  • DominoPlots extend visualization capabilities with binary comparison features.
  • The proposed methods offer an intuitive approach to complex data representation.

Conclusions:

  • DicePlots and DominoPlots address the challenge of visualizing multidimensional categorical data.
  • These visualizations are particularly beneficial for gene and pathway analysis in life sciences.
  • The developed R and Python packages facilitate the application of these novel visualization methods.