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Statgraphics01:10

Statgraphics

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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Updated: Aug 4, 2025

Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature
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qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data.

Bryan Queme1, John C Braisted2, Patricia Dranchak1

  • 1National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.

Journal of Cheminformatics
|April 2, 2023
PubMed
Summary
This summary is machine-generated.

Quantitative high-throughput screening (qHTS) generates complex data. We developed qHTS Waterfall Plots, a flexible software solution using R, to visualize this data effectively for drug discovery and chemical biology.

Keywords:
3-axis plotsConcentration–responseDose–responseEfficacyPharmacologyQuantitative high-throughput screeningqHTS Waterfall plots

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Area of Science:

  • Drug Discovery
  • Chemical Biology
  • Bioinformatics

Background:

  • High-throughput screening (HTS) is crucial for identifying pharmacologically active compounds from large libraries.
  • Standard HTS data visualization (% activity vs. compound ID) is limited.
  • Quantitative HTS (qHTS) generates complex, multi-dimensional data (e.g., concentration-response curves) that is difficult to display.

Purpose of the Study:

  • To address the challenge of visualizing complex qHTS data.
  • To introduce a flexible software solution for plotting complete qHTS datasets.
  • To enable rapid and comprehensive data analysis in drug discovery.

Main Methods:

  • Development of a novel 3-axis plotting method called qHTS Waterfall Plots.
  • Implementation of the plotting method as an R package and an R shiny application.
  • General applicability to any 3-axis dataset.

Main Results:

  • Successful creation of qHTS Waterfall Plots for visualizing complete qHTS data sets.
  • Provision of a flexible and rapid plotting solution.
  • Availability of the software as accessible R tools.

Conclusions:

  • qHTS Waterfall Plots offer an effective method for displaying complex concentration-response data.
  • The developed R package and shiny application facilitate the analysis of qHTS data.
  • This visualization tool enhances the utility of qHTS in drug discovery and chemical biology research.