scmkl 0.4.2
Single-cell analysis using Multiple Kernel Learning
Requires Python: <3.13,>=3.11.1

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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:

```bash
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:
```bash
# 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](https://github.com/ohsu-cedar-comp-hub/scMKL/tree/main/example/README.md).


## Links
Repo: [https://github.com/ohsu-cedar-comp-hub/scMKL](https://github.com/ohsu-cedar-comp-hub/scMKL)

PyPI: [https://pypi.org/project/scmkl/](https://pypi.org/project/scmkl/)

Anaconda: [https://anaconda.org/ivango17/scmkl](https://anaconda.org/ivango17/scmkl)

API: [https://ohsu-cedar-comp-hub.github.io/scMKL/](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](https://huggingface.co/spaces/scMKL-team/scMKL_analysis)


## Issues

Please report bugs [here](https://github.com/ohsu-cedar-comp-hub/scMKL/issues).