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Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https:\/\/github.com\/Masonze\/scLEGA-main.<\/jats:p>","DOI":"10.1093\/bib\/bbae371","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T03:50:31Z","timestamp":1722052231000},"source":"Crossref","is-referenced-by-count":23,"title":["scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2283-2692","authenticated-orcid":false,"given":"Zhenze","family":"Liu","sequence":"first","affiliation":[{"name":"Aulin College, Northeast Forestry University 150040 , 26 Hexing Road, Xiangfang District, Harbin , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjian","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Hepatosplenic Surgery , Ministry of Education, Department of General Surgery, , Harbin , 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