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

> https://rpubs.com/tingtingzhan/groupedHyperframe

documents

- the creation of grouped hyper data frame;
- the creation of a grouped hyper data frame with one-and-only-one point-pattern hypercolumn;
- the 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 from one or more function-value-table hypercolumn(s) of a (grouped) hyper data frame;
- the aggregation of summary statistics, over a (nested) grouping structure, in a grouped hyper data frame.

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

- R01CA222847 (I. Chervoneva, T. Zhan, and H. Rui)
- R01CA253977 (H. Rui and I. Chervoneva).

# 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.