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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Updated: Sep 3, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Graphia: A platform for the graph-based visualisation and analysis of high dimensional data.

Tom C Freeman1,2, Sebastian Horsewell2, Anirudh Patir1

  • 1The Roslin Institute, Easter Bush Campus, The University of Edinburgh, Edinburgh, United Kingdom.

Plos Computational Biology
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Graphia is an open-source platform for graph-based analysis of biological data, enabling visualization and interpretation of complex genomic, proteomic, and cellular information. It facilitates correlation matrix calculations and offers extensive tools for exploring high-dimensional datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Modern biological research generates vast amounts of quantitative and qualitative data from genomics, proteomics, metabolomics, and cell studies.
  • Analyzing these high-dimensional datasets and complex biological networks presents significant computational challenges.

Purpose of the Study:

  • To introduce Graphia, an open-source platform designed for graph-based analysis of large-scale biological data.
  • To provide a comprehensive solution for calculating correlation matrices, visualizing large graphs, and exploring complex biological networks.

Main Methods:

  • Graphia supports the calculation of correlation matrices from continuous or discrete tabular data.
  • The platform enables rapid visualization of large graphs in 2D or 3D space.
  • It offers a suite of measurement algorithms, graph transformation routines, and visualization options for node and edge attributes.

Main Results:

  • Graphia provides a powerful solution for interpreting high-dimensional biological data and network information.
  • The platform is demonstrated through several use cases showcasing its application in biological data analysis.
  • It is extensible via plugins and runs on major desktop operating systems.

Conclusions:

  • Graphia offers a versatile and powerful tool for researchers working with complex biological data.
  • Its open-source nature and extensive functionalities promote wider adoption and application in bioinformatics and computational biology.