ArticleBMC genomics2025
scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles.Briefings in bioinformatics · 2026Article
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- Mechanisms by which carbamoylated high-density lipoprotein (C-HDL) promotes calcific aortic valve disease and exploration of potential targeted therapies.Open life sciences · 2026Article
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- ViViMZheimer a slice based end to end model for Alzheimer's disease diagnosis from 3D MRI.Scientific reports · 2025Article
- Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network.Scientific reports · 2025Article
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4 authors.
Funding
Abstract
backgroundClustering 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.
resultsWe 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) model and ZI layer. Based on ZINB model, we introduce autoencoder and SimAM to reduce dimensionality of data and learn feature representations of cells and relationships between cells. Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. Experimental results demonstrate that scAMZI outperforms competing methods.
conclusionsscAMZI outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://doi.org/10.5281/zenodo.13131559 .
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