Welcome to scACCorDiON's documentation!

we present scACCorDiON$$^{1,2}$$ (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Additionally, we derive a k-barycenter algorithm using the Wasserstein-based distance, which is able to cluster directed weighted graphs. We compare our approach with competing methods in several large cohorts of scRNA-seq data. Our results show that scACCorDiON can predict clusters better, matching the disease status of samples. Moreover, we show that barycenters provide a robust and explainable representation of cell cell communication events related to the detected clusters. We also provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment.

[1] Nagai, J. S., Costa, I. G., & Schaub, M. T. (2024, April). Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 9856-9860). IEEE.

[2] Nagai, J. S., Schaub, M. T., & Gesteira Costa Filho, I. (2024). scACCorDiON: A clustering approach for explainable patient level cell cell communication graph analysis. bioRxiv, 2024-08.