Supervised Learning Tools for Deriving Biomarkers based on Single-Cell Data

documents the derivation of single index predictors of scalar outcomes based on spatial and non-spatial single-cell imaging data.

2 Prerequisite

2.1 Environment

Package hyper.gam (v0.2.0) 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

2.2 Enhancement & Dependency

Package hyper.gam (v0.2.0) Enhances package mgcv (Wood 2017, v1.9.3). Details are provided in the complete vignette.

The dependencies of package hyper.gam are detailed in the complete vignette.

Package hyper.gam requires the development versions of the spatstat.* family of packages (Baddeley, Rubak, and Turner 2015; Baddeley and Turner 2005). Installation instructions are provided in the complete vignette of package groupedHyperframe (Zhan and Chervoneva 2025, v0.3.0), Section 4.1.

2.3 Installation

Packages hyper.gam (v0.2.0) and groupedHyperframe (v0.3.0) can be installed using the following command.

utils::install.packages('groupedHyperframe')
utils::install.packages('hyper.gam')

2.4 Getting Started

library(groupedHyperframe)
library(hyper.gam)
library(survival)

2.5 Acknowledgement

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

3 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/.

Wood, S. N. 2017. Generalized Additive Models: An Introduction with R. 2nd ed. Chapman; Hall/CRC.

Zhan, Tingting, and Inna Chervoneva. 2025. groupedHyperframe: Grouped Hyper Data Frame: An Extension of Hyper Data Frame Object. https://doi.org/10.32614/CRAN.package.groupedHyperframe.

1 Introduction