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Background <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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scZGA: a novel model based on ZINB distribution and graph attention for scRNA-seq data clustering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Yansheng Kan,
  • Yuling Liu,
  • Jiacheng Pan,
  • Ruochen Wang,
  • Chen-Yu Zhang,
  • Zhen Zhou

摘要

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.