scEMC

This is the source code for scEMC: Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data. We propose an effective multi-modal clustering model scEMC for parallel scRNA and scATAC data. Concretely, we have devised a skip aggregation network (SAN) to simultaneously learn global structural information among cells and integrate data from diverse modalities. This manuscript is currently under peer review for publication in the journal "Briefings in Bioinformatics". If you have any questions about the code, please send an email to hzauhdy@gmail.com

Requirements

Python --- 3.7.0

Numpy --- 1.21.6

Torch --- 1.13.1

Scikit-learn --- 1.0.2

Scipy --- 1.7.3

Scanpy --- 1.9.3

Guidance for running code

The Source of datasets

An example dataset, named 'BMNC.mat', has been provided. Furthermore, the sources for the other datasets are provided within our manuscript:

Examples

The example data for the BMNC dataset, 'BMNC.mat', is located in the 'scEMC/datasets' directory. The MAT format was chosen for data storage due to its efficient compression capabilities. If modification of the dataset is required, please make the necessary changes in the 'load_data.py' file. Should there be a need to adjust additional parameters, they can be configured in the 'parse_arguments' function within the 'scEMC.py' file.

parser = argparse.ArgumentParser(description='scEMC')
parser.add_argument('--n_clusters', default=data_para['K'], type=int)
parser.add_argument('--lr', default=1, type=float)
# ... other arguments ...

Run

python scEMC.py