scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.
Journal:
BMC genomics
PMID:
40197174
Abstract
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they are developed based on gene expression information and lack of flexibility in integrating intercellular relationships. Finally, the performance of these methods is affected by dropout event.