Grouped Hyper Data Frame

Tingting Zhan

2025-10-12

The complete vignette of R package groupedHyperframe (v0.3.0)

https://rpubs.com/tingtingzhan/groupedHyperframe

documents

The complete vignette exceeds the file size limit allowed on CRAN.

1 Prerequisite

1.1 Environment

Package groupedHyperframe (v0.3.0) requires R version 4.5.0 (released 2025-04-11) or higher (macOS, Windows, Linux).

An Integrated Development Environment (IDE), e.g., RStudio (Posit team 2025) or Positron, is not required, but highly recommended.

Environment on author’s computer 
Sys.info()[c('sysname', 'release', 'machine')]
#  sysname  release  machine 
# "Darwin" "25.1.0"  "arm64"
R.version
#                _                           
# platform       aarch64-apple-darwin20      
# arch           aarch64                     
# os             darwin20                    
# system         aarch64, darwin20           
# status                                     
# major          4                           
# minor          5.1                         
# year           2025                        
# month          06                          
# day            13                          
# svn rev        88306                       
# language       R                           
# version.string R version 4.5.1 (2025-06-13)
# nickname       Great Square Root

1.2 Enhancement & Dependency

Package groupedHyperframe (v0.3.0) Enhances the spatstat.* family of packages (Baddeley, Rubak, and Turner 2015; Baddeley and Turner 2005), especially spatstat.geom and spatstat.explore. Details are provided in the complete vignette, Section 4.1.

The dependencies of package groupedHyperframe are detailed in the complete vignette, Section 4.1.

Package groupedHyperframe requires the development versions of the spatstat.* family of packages. Installation instructions are provided in the complete vignette, Section 4.1.

1.3 Installation

Package groupedHyperframe (v0.3.0) can be installed using the following command.

utils::install.packages('groupedHyperframe')

2 Getting Started

Examples in the complete vignette require that the search path has

library(groupedHyperframe)
library(survival)

3 Peer-Reviewed Publications

Package groupedHyperframe (v0.3.0) plays a pivotal role in these peer reviewed publications from the authors.

3.1 Zhan, Yi, and Chervoneva (2025)

Zhan T, Yi M, Chervoneva I (2025). “Quantile Index predictors using R package hyper.gam.” Bioinformatics, 41(8), btaf430. ISSN 1367-4811, doi:10.1093/bioinformatics/btaf430 https://doi.org/10.1093/bioinformatics/btaf430.

BibTeX and/or BibLaTeX entries for LaTeX users 
Warning in citation(package = "groupedHyperframe"): could not determine year
for 'groupedHyperframe' from package DESCRIPTION file
@Article{,
  title = {Quantile Index predictors using R package `hyper.gam`},
  author = {Tingting Zhan and Misung Yi and Inna Chervoneva},
  journal = {Bioinformatics},
  volume = {41},
  number = {8},
  pages = {btaf430},
  year = {2025},
  month = {07},
  issn = {1367-4811},
  doi = {10.1093/bioinformatics/btaf430},
}

@Manual{,
  title = {groupedHyperframe: Grouped Hyper Data Frame: An Extension of
Hyper Data Frame},
  author = {Tingting Zhan and Inna Chervoneva},
  note = {R package version 0.3.0},
  url = {https://github.com/tingtingzhan/groupedHyperframe},
}

as well as Yi et al. (2025); Yi et al. (2023b); Yi et al. (2023a), was featured with a hyper data frame Ki67q with a numeric-hypercolumn logKi67.quantile. Functions in the R code-chunk below are explained in the complete vignette.

R code in Zhan, Yi, and Chervoneva (2025) 
Ki67q = groupedHyperframe::Ki67 |>
  within.data.frame(expr = {
    x = y = NULL # remove x- and y-coords for non-spacial application
  }) |>
  as.groupedHyperframe(group = ~ patientID/tissueID) |> 
  quantile(probs = seq.int(from = .01, to = .99, by = .01)) |> 
  aggregate(by = ~ patientID)
A hyperframe object Ki67q: aggregated quantiles 
Ki67q |>
  head()
# Hyperframe:
#   Tstage  PFS adj_rad adj_chemo histology  Her2   HR  node  race age patientID
# 1      2 100+   FALSE     FALSE         3  TRUE TRUE  TRUE White  66   PT00037
# 2      1   22   FALSE     FALSE         3 FALSE TRUE FALSE Black  42   PT00039
# 3      1  99+   FALSE        NA         3 FALSE TRUE FALSE White  60   PT00040
# 4      1  99+   FALSE      TRUE         3 FALSE TRUE  TRUE White  53   PT00042
# 5      1  112    TRUE      TRUE         3 FALSE TRUE  TRUE White  52   PT00054
# 6      4   12    TRUE     FALSE         2  TRUE TRUE  TRUE Black  51   PT00059
#   logKi67.quantile
# 1        (numeric)
# 2        (numeric)
# 3        (numeric)
# 4        (numeric)
# 5        (numeric)
# 6        (numeric)

Readers are encouraged to learn more about this application from package hyper.gam (Zhan and Chervoneva 2025, CRAN, Github) vignette, section Quantile Index.

4 Acknowledgement

This work is supported by National Institutes of Health, U.S. Department of Health and Human Services grants

5 References

Baddeley, Adrian, Ege Rubak, and Rolf Turner. 2015. Spatial Point Patterns: Methodology and Applications with R. London: Chapman; Hall/CRC Press. https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200/.
Baddeley, Adrian, and Rolf Turner. 2005. “spatstat: An R Package for Analyzing Spatial Point Patterns.” Journal of Statistical Software 12 (6): 1–42. https://doi.org/10.18637/jss.v012.i06.
Posit team. 2025. RStudio: Integrated Development Environment for R. Boston, MA: Posit Software, PBC. https://posit.co/.
Yi, Misung, Tingting Zhan, Amy R. Peck, Jeffrey A. Hooke, Albert J. Kovatich, Craig D. Shriver, Hai Hu, Yunguang Sun, Hallgeir Rui, and Inna Chervoneva. 2023a. “Quantile Index Biomarkers Based on Single-Cell Expression Data.” Laboratory Investigation 103 (8): 100158. https://doi.org/10.1016/j.labinv.2023.100158.
———. 2023b. “Selection of Optimal Quantile Protein Biomarkers Based on Cell-Level Immunohistochemistry Data.” BMC Bioinformatics 24 (1): 298. https://doi.org/10.1186/s12859-023-05408-8.
Yi, Misung, Tingting Zhan, Hallgeir Rui, and Inna Chervoneva. 2025. “Functional Protein Biomarkers Based on Distributions of Expression Levels in Single-Cell Imaging Data.” Bioinformatics, April, btaf182. https://doi.org/10.1093/bioinformatics/btaf182.
Zhan, Tingting, and Inna Chervoneva. 2025. hyper.gam: Generalized Additive Models with Hyper Column. https://CRAN.R-project.org/package=hyper.gam.
Zhan, Tingting, Misung Yi, and Inna Chervoneva. 2025. “Quantile Index Predictors Using r Package ‘Hyper.gam‘.” Bioinformatics, July, btaf430. https://doi.org/10.1093/bioinformatics/btaf430.