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scZGA: a novel model based on ZINB distribution and graph attention for scRNA-seq data clustering
Yansheng Kan1,2,3, Yuling Liu1,2,3, Jiacheng Pan1,2,3
1Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, School of Life Sciences, NJU Advanced Institute of Life Sciences (NAILS), Nanjing University, Nanjing, 210023, Jiangsu, China.
Background:
Identifying different cell types is a prerequisite step in the analysis of single-cell RNA sequencing (scRNA-seq) data, with clustering being a common technique utilized for this purpose. However, high dropout rates inherent in scRNA-seq data and complex intercellular relationships become main challenges in scRNA-seq data analysis.
Results:
To address these issues, we proposed a novel model based on zero-inflated negative binomial (ZINB) distribution and graph attention network for scRNA-seq data clustering (scZGA). scZGA consists of three key modules. The first module captures the global probabilistic structure using a ZINB model. The second module constructs the graph with Pearson's correlation coefficient, and employs a graph autoencoder with residual connection to learn important neighbor relationships while preserving topological structure information simultaneously. The final module conducts deep clustering through a self-optimizing embedding algorithm.
Conclusions:
With these improvements, clustering results show that scZGA consistently achieves higher scores across six scRNA-seq datasets by using evaluation metrics such as normalized mutual information and adjusted rand index.
