refcm 1.0.1

RefCM is a computational method that combines optimal transport and integer programming to enhance the annotation of scRNA clusters using established reference datasets.

RefCM: Reference Cluster-Mapping

RefCM is an automated tool for cell type annotation across single-cell RNA-seq datasets. It leverages optimal transport to align cell-type clusters across tissues, sequencing technologies, and species.

This repository supports reproducing the results from our paper and helps others apply RefCM to their own datasets.

Table of Contents

• Table of Contents
• About
• Installation
• Requirements
• Usage
• Examples
• Citation & Contact

About

The primary purpose of this repository is to enable the reproduction of the results reported in the paper, and help others utilize this method towards their own work.

Installation

RefCM is available on PyPI. You can install it using pip:

pip install refcm

If you want to build from source and/or run our benchmarking suite, you can use uv:

uv sync --all-extras

Note: RefCM depends on the GLPK solver.

• On macOS, install via Homebrew with brew install glpk.
• On Windows, follow this guide.

After installation, restart your IDE or terminal to ensure the solver is recognized.

Requirements

The package has been tested on:

• macOS Sequoia (Apple M1 Pro, 32 GB RAM)
• Windows 11 (Intel i5 4-core CPU, 8 GB RAM)
• Ubuntu 24.04 (NVIDIA GH200, 480 GB RAM)

There are no strict hardware requirements, aside from the ability to load the query and reference datasets into memory. All analyses, except for the large embryogenesis datasets, were reproducible on the 8 GB Windows system. On the M1 Pro, even the largest datasets completed in under 10 minutes.

Usage

Running RefCM on a given query: AnnData and reference: AnnData dataset pair, assuming clustering information under their respective .obs['cluster'] attributes, can be done as follows. The method expects log-normalized counts to be provided under each dataset's .X attribute.

from refcm import RefCM

rcm = RefCM()
rcm.setref(reference, 'cluster')
rcm.annotate(query, 'cluster')

The resulting annotations are written out to the .obs['refcm'] field in the query's AnnData object, metadata in .uns['refcm'], leaving the remaining fields unchanged.

Examples

We’ve put together a few example Jupyter notebooks under the vignettes folder.

We recommend starting with brain.ipynb, which applies RefCM to the Allen Brain Atlas.

Citation & Contact

If you use RefCM in your work, please consider citing our paper (link coming soon).
Feel free to open an issue or pull request — contributions and feedback are welcome!