Rphenograph

R implementation of the PhenoGraph algorithm — a clustering method designed for high-dimensional single-cell data analysis. It works by creating a graph representing phenotypic similarities between cells by calculating the Jaccard coefficient between nearest-neighbor sets, and then identifying communities using the Louvain method.

This is a maintained fork of JinmiaoChenLab/Rphenograph with fixes for modern R (>= 4.1) and igraph (>= 2.0).

Installation

# Install from GitHub (recommended)
pak::pkg_install("i-cyto/Rphenograph")

# Or with remotes
remotes::install_github("i-cyto/Rphenograph")

Apple Silicon (M1/M2/M3/M4) notes

The package compiles cleanly on ARM64 Macs. If you hit issues:

1. Install/update Xcode Command Line Tools:

   xcode-select --install

2. Check ~/.R/Makevars for stale x86 flags. If the file contains -mtune=core2 or -march=core2, remove or comment out those lines. These are Intel-only flags that break ARM64 compilation.

3. Ensure R itself is ARM64-native. In R, run R.version$arch — it should say aarch64, not x86_64. If it says x86_64, download the native ARM64 build from https://cran.r-project.org/bin/macosx/.

Windows (no compiler needed)

install.packages(
  "https://github.com/i-cyto/Rphenograph/releases/download/Rphenograph_0.99.1.9004/Rphenograph_0.99.1.9004.zip",
  repos = NULL, type = "win.binary"
)

Usage

library(Rphenograph)
library(igraph)

iris_unique <- unique(iris)
data <- as.matrix(iris_unique[, 1:4])
out <- Rphenograph(data, k = 45)

# Cluster memberships
membership(out[[2]])

# Modularity
modularity(out[[2]])

Reference

Levine JH, Simonds EF, Bendall SC, Davis KL, Amir ED, Tadmor MD, et al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 2015. https://doi.org/10.1016/j.cell.2015.05.047