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.
# install.packages("remotes")
remotes::install_github("mhurtado13/racing")
library(RaCInG)# install.packages("devtools")
devtools::install(".")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| 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 |
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
)- 📘 Vignette: Getting started with RaCInG
- 🌐 Website: https://mhurtado13.github.io/racing/
- 🐍 Original Python implementation: https://github.com/SysBioOncology/RaCInG
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.
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.