GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Gastric cancer (GC) can be divided into gene expression-based and biologically distinct molecular subtypes. This package is to built to predict gastric cancer samples based on their log2 scaled gene expression profiles (GEP). More information about the usage of GCclassifier could be found by running browseVignettes('GCclassifier').

Installing the package in RStudio is recommended, since RStudio makes it easier for an average user to work with R Markdown and the vignette of GCclassifier is written in R Mardown format and the Pandoc [http://pandoc.org/] is required for the vignette creating [https://bookdown.org/yihui/rmarkdown/installation.html]. The RStudio IDE can be downloaded via this link https://posit.co/download/rstudio-desktop/. The package installation requires a minimum R version of 4.1.0.

Install package

  # Required packages: run if not already installed
  if(!requireNamespace('BiocManager')){
    install.packages('BiocManager')
  }
  if(!requireNamespace('devtools')){
    install.packages('devtools')
  }

  ## Users need to install these packages before using GCclassifier
  BiocManager::install(c(
    'impute', 'dplyr', 'magrittr', 'AnnotationDbi', 'randomForest', 
    'org.Hs.eg.db', 'shiny', 'DT', 'shinyjs', 'BiocStyle', 'survminer'), force = T)
  
  ### install: latest version (R version >= 4.1.0 required)
  ### build_vignettes = T, if installing using RStudio
  devtools::install_github("Ronlee12355/GCclassifier", build_vignettes = T)
  
  ### if not installing from RStudio IDE, no vignette creating is recommended since it requires Pandoc and other dependancies
  devtools::install_github("Ronlee12355/GCclassifier", build_vignettes = F)

Quick start

  library(GCclassifier)
  data("GSE62254")
  
  emp.res <- classifyGC(
    Expr = GSE62254, ## gene expression profiles with log2 transformation
    method = 'EMP', ## subtyping system
    idType = 'SYMBOL' ## the gene identifier type in gene expression profiles
  )

  acrg.res <- classifyGC(
    Expr = GSE62254, 
    method = 'ACRG', 
    idType = 'SYMBOL' 
  )
  
  tcga.res <- classifyGC(
    Expr = GSE62254, 
    method = 'TCGA', 
    idType = 'SYMBOL'
  )

Internal Shiny application
To extend the usage of GCclassifier, a build-in Shiny application was developed and users could obtain the subtype prediction results simply by uploading their gene expression profiles.

library(GCclassifier)
classifyGC_interface()

Supported browers:

• Chrome (recommended)
• Firefox
• Safari
• Microsoft Edge

Design
GCclassifier package builds on standard R package workflow and was developed in RStudio following guidelines in R packages [http://r-pkgs.had.co.nz/].

Citation
Jiang Li, Lingli He, Xianrui Zhang, Xiang Li, Lishi Wang, Zhongxu Zhu, Kai Song, Xin Wang. GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer. Comput. Struct. Biotechnol. J. 2024; 23: 752-758. https://doi.org/10.1016/j.csbj.2024.01.010

References
Oh, S.C., Sohn, B.H., Cheong, JH. et al. Clinical and genomic landscape of gastric cancer with a mesenchymal phenotype. Nat Commun 9, 1777 (2018). https://doi.org/10.1038/s41467-018-04179-8

Cristescu, R., Lee, J., Nebozhyn, M. et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med 21, 449--456 (2015). https://doi.org/10.1038/nm.3850

The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202--209 (2014). https://doi.org/10.1038/nature13480

R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2013. http://www.R-project.org/

Wickham, H. R Packages: Organize, Test, Document, and Share Your Code. 1st ed. O'Reilly Media. 2015.