multiRF

Fast multivariate random forests for multi-omics integration

multiRF is an R package for integrating matched multi-omics datasets with multivariate random forests (MRF). It fits directed forest models across omics blocks, learns sample-by-sample similarity from shared terminal-node structure, and decomposes the result into shared and omics-specific components for clustering, variable selection, and visualization.

The package now uses a native C++ backend for multivariate regression, unsupervised forests, forest weights, proximity matrices, and enhanced proximity with sibling-leaf corrections. In practice, this gives a simpler installation path and a faster MRF than the randomForestSRC-based MRF while keeping the same overall modeling logic.

Project website: https://novawz.github.io/multiRF/

Installation

remotes::install_github("novawz/multiRF")

The package compiles from source and requires a C++17 toolchain:

• macOS: Xcode Command Line Tools
• Windows: Rtools
• Linux: g++ or clang++

OpenMP is recommended for parallel tree construction. randomForestSRC is not required for the default workflow.

Quick start

library(multiRF)
data("tcga_brca_data")

names(tcga_brca)
#> [1] "gene"  "methy" "mirna"

fit <- mrf3(
  tcga_brca,
  k = 4,
  ntree = 100,
  filter_mode = "none",
  run_imd = TRUE,
  seed = 529
)

summary(fit)
table(get_clusters(fit))
get_top_vars(fit, n = 10)

mrf3() is the main user-facing entry point. It wraps the staged workflow in mrf3_fit() and forwards advanced arguments through ....

What it provides

• mrf3(): end-to-end workflow for fitting, reconstruction, and clustering
• mrf3_fit(): staged workflow with the full parameter surface exposed
• mrf3_vs(): variable selection from IMD weights
• mrf3_stability(): resampling-based cluster stability assessment
• pairwise_imd(): variable-level co-occurrence network analysis
• plot_tsne(), plot_umap(), plot_network(), plot_km(): downstream visualization helpers

Clustering modes

fit_sim <- mrf3(
  tcga_brca,
  k = 4,
  ntree = 100,
  main_clustering = "similarity", # default
  seed = 529
)

fit_prox <- mrf3(
  tcga_brca,
  k = 4,
  ntree = 100,
  main_clustering = "proximity",
  seed = 529
)

fit_enh <- mrf3(
  tcga_brca,
  k = 4,
  ntree = 100,
  main_clustering = "enhanced_proximity",
  seed = 529
)

Full workflow example

fit_full <- mrf3(
  tcga_brca,
  k = 4,
  ntree = 200,
  run_imd = TRUE,
  run_variable_selection = TRUE,
  run_robust_clustering = TRUE,
  variable_selection_args = list(method = "mixture"),
  model_top_v = 50,
  fused_top_v = 30,
  seed = 529
)

Bundled data

• tcga_brca: TCGA BRCA example with gene, methy, and mirna blocks
• tcga_brca_clinical: matched clinical annotations including subtype and survival information

Citation

If you use multiRF in your research, please cite:

Zhang, W., Wang, L., Franzmann, E. J., and Chen, X. S. (2026). Multivariate Random Forests for Cross-Modal Multi-Omics Integration. bioRxiv. doi:10.64898/2026.06.17.732933

Zhang, W. et al. (2025). An integrative multi-omics random forest framework for robust biomarker discovery. GigaScience, 14, giaf148. doi:10.1093/gigascience/giaf148