Nagai James S, Maié Tiago, Schaub Michael T, Costa Ivan G
Institute for Computational Genomics, RWTH Aachen Medical Faculty, Aachen 52074, Germany.
Department of Computational Science, RWTH Aachen University, Aachen 52074, Germany.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf288.
Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets.
Here, we present scACCorDiON (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. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival.
The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.
将单细胞测序与配体-受体（LR）分析相结合，为表征复杂组织中的细胞通讯事件铺平了道路。特别是，有向加权图自然地表示细胞间通讯事件。然而，目前的计算方法还无法分析在大型患者队列中产生的单细胞数据所测量的样本特异性细胞间通讯事件。基于队列的细胞间通讯分析面临许多挑战，例如细胞间通讯的非线性性质以及患者特异性单细胞RNAseq数据集所带来的高变异性。
在这里，我们提出了scACCorDiON（使用有向网络中的最优传输对疾病簇中的细胞间通讯进行单细胞分析），这是一种最优传输算法，它将马尔可夫链上的节点距离作为有向加权图之间的基础度量来探索。基准测试表明，与使用无向图的竞争方法相比，scACCorDiON根据疾病状态对样本进行了更好的聚类。我们提供了一个胰腺腺癌的案例研究，其中scACCorDion检测到与肿瘤微环境变化相关的疾病样本亚群。我们的研究案例证实，簇为细胞间通讯事件提供了稳健且可解释的表示，并且检测到的LR对的表达可预测胰腺癌的生存情况。
scACCorDiON的代码可在https://scaccordion.readthedocs.io/en/latest/和https://doi.org/10.5281/zenodo.15267648获取。生存分析包可在https://github.com/CostaLab/scACCorDiON.su找到。
