ScAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

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.

Results

We propose a novel deep learning (DL) model based on attention autoencoder and zero-inflated (ZI) layer, namely scAMZI, to cluster scRNA-seq data. scAMZI is mainly composed of SimAM (a Simple, parameter-free Attention Module), autoencoder, ZINB (Zero-Inflated Negative Binomial

SUBMITTER: Yuan L 

PROVIDER: S-EPMC11974017 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature