RaCInG • RaCInG

RaCInG (Random Cell-cell Interaction Generator) reconstructs patient-specific cell-cell communication networks from bulk RNA-seq data and extracts network-level features using either a kernel-based or Monte Carlo workflow.

This package is the R implementation of the original RaCInG Python framework, as described in van Santvoort et al. (2025), repackaged for seamless integration with R/Bioconductor analysis pipelines.

Installation

Install from GitHub

# install.packages("remotes")
remotes::install_github("mhurtado13/racing")
library(RaCInG)

Install from a local clone

# install.packages("devtools")
devtools::install(".")

Optional preprocessing dependencies

If you want to start directly from raw counts with prepare_input_files(), install the optional helper packages used during deconvolution and prior-network construction:

install.packages(c("ggplot2", "OmnipathR"))
# Additional optional packages used by the full preprocessing workflow:
# ADImpute, multideconv, liana

Workflow at a glance

Goal	Function	Output
Build input matrices from raw counts	`prepare_input_files()`	Named list with `L`, `R`, `C`, `LR` matrices and labels
Run deterministic features	`compute_racing_kernel()`	Kernel arrays + feature matrix
Run simulation-based features	`compute_racing_montecarlo()`	Monte Carlo summaries
Compare patient groups	`wilcox_group_test()`	Statistics table for downstream plots

Quick start

library(RaCInG)

# Build input matrices from raw counts
input <- prepare_input_files(
  counts = counts_matrix,
  output_folder = "Results/",
  file_name = "example"
)

# Run kernel method (from raw counts)
kernel_res <- compute_racing_kernel(
  counts = counts_matrix,
  file_name = "example",
  output_folder = tempdir(),
  communication_type = "W"
)

# Or pass pre-computed inputs to skip preprocessing
kernel_res <- compute_racing_kernel(
  input_data = input,
  communication_type = "W"
)

# Monte Carlo method
mc_res <- compute_racing_montecarlo(
  input_data = input,
  file_name = "example",
  output_folder = tempdir(),
  communication_type = "W",
  Ncells = 100,
  Ngraphs = 10,
  Ndegree = 3
)

Documentation

📘 Vignette: Getting started with RaCInG
🌐 Website: https://mhurtado13.github.io/racing/
🐍 Original Python implementation: https://github.com/SysBioOncology/RaCInG

Citation

If you use this package, please cite the RaCInG publication:

van Santvoort M, Lapuente-Santana Ó, Zopoglou M, Zackl C, Finotello F, van der Hoorn P & Eduati F (2025). Mathematically mapping the network of cells in the tumor microenvironment. Cell Reports Methods, 5(2), 100985.

R Package Development

This R package implementation was developed by Marcelo Hurtado from the Pancaldi team, led by Vera Pancaldi. Marcelo is currently the primary maintainer of the package.