graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data.  The results indicate that although there is no consistently best method across all the analyzed datasets, graph-sc compares favorably to competing techniques across all types of datasets.  https://github.com/ciortanmadalina/graph-sc.



graph-sc
graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data. 
graph autoencoder network
graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data. 
scRNA-seq cell data
While this work analyzes the performance of clustering the embeddings with various clustering algorithms, other downstream tasks can also be performed. 
clustering algorithms
Furthermore, the proposed method is stable across consecutive runs, robust to input down-sampling, generally insensitive to changes in the network architecture or training parameters and more computationally efficient than other competing methods based on neural networks. 
neural networks