A universal framework for single-cell multi-omics data integration with graph convolutional networks A single-cell omics data integration algorithm based on graph convolutional neural networks. With the help of graph convolutional neural network, it can not only remove batch effects between different sequencing methods, omics, and species, but also explore the nonlinear relationship between cells in single-cell omics data and effectively integrate data.
###GCN-SC uses the following dependencies:
- python 3.7.10 *pytorch 1.8.0
- numpy 1.15.5
- scikit-learn 0.24.2
- pandas 1.3.1
- nimfa 1.4.0
###Guiding principles:
**MNN
adj.py
Finding internal anchor pairs for query omics data
mixadj.py
Finding anchor pairs between query omics and reference omics data
**scImpute
scimpute.R
Imputed transcriptome data
**GCN
utils.py
Definition of base class
layers.py
Definition of the classes used by the model
models.py
Definition of the model
train.py
Running
**NMF:
nmf.py
Dimensionality reduction algorithm