Single-cell analysis using Multiple Kernel Learning, scMKL, is a binary classification algorithm utilizing prior information to group features to enhance classification and aid understanding of distinguishing features in multi-omic data sets.

Installation

Conda install

Conda is the recommended method to install scMKL:

conda create -n scMKL python=3.12 
conda activate scMKL
conda install -c conda-forge -c bioconda ivango17::scmkl

Ensure bioconda and conda-forge are in your available conda channels.

Pip install

First, create a virtual environment with python>=3.11.1,<3.13.

Then, install scMKL with:

# activate your new env with python>=3.11.1 and <3.13
pip install scmkl

If wheels do not build correctly, ensure gcc and g++ are installed and up to date. They can be installed with sudo apt install gcc and sudo apt install g++.

Requirements

scMKL takes advantage of AnnData objects and can be implemented with just four pieces of data:

1. scRNA and/or scATAC matrices (can be scipy.sparse matrix)

2. An array of cell labels

3. An array of feature names (eg. gene symbols for RNA or peaks for ATAC)

4. A grouping dictionary where {'group_1' : [feature_5, feature_16], 'group_2' : [feature_1, feature_4, feature_9]}

For implementing scMKL and learning how to get meaningful feature groupings, see our examples for your use case in examples.

Links

Repo: https://github.com/ohsu-cedar-comp-hub/scMKL

PyPI: https://pypi.org/project/scmkl/

Anaconda: https://anaconda.org/ivango17/scmkl

API: https://ohsu-cedar-comp-hub.github.io/scMKL/

Publication

If you use scMKL in your research, please cite using:

Kupp, S., VanGordon, I., Gönen, M., Esener, S., Eksi, S., Ak, C. Interpretable and integrative analysis of single-cell multiomics with scMKL. Commun Biol 8, 1160 (2025). https://doi.org/10.1038/s42003-025-08533-7

Our Shiny for Python application for viewing data produced from this work can be found here: scMKL_analysis

Issues

Please report bugs here.