Grouped Hyper Data Frame

Author

Tingting Zhan

Published

November 25, 2025

Preface

Mirrors of this Quarto book can be accessed at the following URLs. These free hosting services may experience occasional downtime. Please refer to the most recent version of the book.

https://tingtingzhan.quarto.pub/groupedhyperframe/

https://tingtingzhan-groupedhyperframe.netlify.app

Most of this work was undertaken during Tingting Zhan’s leisure hours, with limited support from National Institutes of Health, U.S. Department of Health and Human Services grants

The author thanks

  • Erjia Cui’s contribution to function hyper.gam::hyper_gam().

Single-cell multiplex immuno-fluorescence immunohistochemistry (mIF-IHC) imaging data are the result of digital processing of the microscopic images of tissue stained with selected antibodies. Quantitative pathology platforms, e.g., Akoya or QuPath, support cell segmentation of mIF-IHC images and quantification of the mean protein expression in each cell. The cell centroid coordinates and cell signal intensities (CSIs) for each stained protein are usually extracted as individual comma-separated values .csv files. For each cell in a tissue image, the data include the cell centroid coordinates and cell signal intensity (CSI) for each quantified protein expression. The data may have multiple levels of hierarchical clustering. For example, single cells are clustered within a Region of Interest (ROI) or a tissue core, ROIs are clustered within a tissue or tissue cores are clustered within a patient.

Deriving single index predictors of scalar outcomes based on spatial and non-spatial single-cell imaging data …blah blah…

Some more English

The author present a collections of packages (these packages)

BibTeX and/or BibLaTeX entries for LaTeX users 
@Manual{,
  title = {groupedHyperframe: Grouped Hyper Data Frame: An Extension of
Hyper Data Frame},
  author = {Tingting Zhan},
  note = {R package version 0.3.2},
  url = {https://github.com/tingtingzhan/groupedHyperframe},
}

@Manual{,
  title = {groupedHyperframe.random: Simulated Grouped Hyper Data Frame},
  author = {Tingting Zhan},
  note = {R package version 0.2.0.20251031},
  url = {https://tingtingzhan.quarto.pub/groupedhyperframe/random},
}

@Manual{,
  title = {hyper.gam: Generalized Additive Models with Hyper Column},
  author = {Tingting Zhan and Inna Chervoneva},
  year = {2025},
  note = {R package version 0.2.0},
  url = {https://CRAN.R-project.org/package=hyper.gam},
  doi = {10.32614/CRAN.package.hyper.gam},
}

@Manual{,
  title = {maxEff: Additional Predictor with Maximum Effect Size},
  author = {Tingting Zhan and Inna Chervoneva},
  note = {R package version 0.2.1},
  url = {https://github.com/tingtingzhan/maxEff},
}

This Quarto book documents

  • the creation of grouped hyper data frame (2  Grouped Hyper Data Frame);
  • the creation of a grouped hyper data frame with one-and-only-one point-pattern hypercolumn (Creation);
  • the batch process on eligible marks (Batch Process on Eligible Marks) for the one-and-only-one point-pattern hypercolumn in a (grouped) hyper data frame;
  • the computation of various summary statistics (Summarization) from one or more function-value-table hypercolumn(s) of a (grouped) hyper data frame;
  • the aggregation (Aggregation) of summary statistics, over a (nested) grouping structure, in a grouped hyper data frame.
  • the simulation of superimposed (marked) point-patterns via vectorized parameterization (Simulated Point-Pattern);
  • the simulation of grouped hyper data frame via matrix parameterization (Simulated Grouped Hyper Data Frame).

The Chapters 1  Introduction, 2  Grouped Hyper Data Frame, 3  Grouping ppp-Hypercolumn, 4  Simulation, 5  Quantile Index and 6  Predictor with Maximum Effect Size of this book explain how to use this package to a general audience.

Rest of this book explain why and how these packages works for readers with advanced expertise in the R programming language.