DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data
Jiang, Q.; Yang, W.; Xu, Z.; Luo, M.; Cai, Y.; Xu, C.; Wang, P.; Wei, S.; Xue, G.; Jing, X.; Cheng, R.; Que, J.; Zhou, W.; Pang, F.; Nie, H.
Show abstract
With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (CCIs) from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. To address the issue, here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively.
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