ArticleBioinformatics (Oxford, England)2024
scMAE: a masked autoencoder for single-cell RNA-seq clustering.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 41 citations in OpenAlex.
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- scMSDA: A Novel Multi-View Fusion Framework for Single-Cell RNA-seq Data Clustering with Semantic and Distribution Alignment.Interdisciplinary sciences, computational life sciences · 2026Article
- Deciphering RNA and protein expression discordance identifies TOP2A as a prognostic biomarker and potential therapeutic target in lung adenocarcinoma.Discover oncology · 2026Article
- scLTF: A self-training clustering framework for large-scale single-cell RNA-Seq data.PloS one · 2026Article
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- Article
- sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.bioRxiv : the preprint server for biology · 2025Article
- The cell as a token: high-dimensional geometry in language models and cell embeddings.Bioinformatics (Oxford, England) · 2025Review
- CluVar: clustering of variants using autoencoder for inferring cancer subclones from single cell RNA sequencing data.Briefings in bioinformatics · 2025Article
- scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning.Briefings in bioinformatics · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data analysis.Bioinformatics (Oxford, England) · 2025Article
- CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.BMC bioinformatics · 2025Article
- scGGC: a two-stage strategy for single-cell clustering through cellular gene pathway construction.Briefings in bioinformatics · 2025Article
- RGCN-BA: relational graph convolutional network with batch awareness for single-cell RNA sequencing clustering.Briefings in bioinformatics · 2025Article
- Deep clustering of single-cell RNA-seq using adversarial graph contrastive learning.Briefings in bioinformatics · 2025Article
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.Scientific reports · 2025Article
- A robust multi-scale clustering framework for single-cell RNA-seq data analysis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationSingle-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes.
resultsHere, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoencoder introduces a masking predictor, which captures relationships among genes by predicting whether gene expression values are masked. By integrating this masking mechanism, scMAE effectively captures latent structures and dependencies in the data, enhancing clustering performance. We conducted extensive comparative experiments using various clustering evaluation metrics on 15 scRNA-seq datasets from different sequencing platforms. Experimental results indicate that scMAE outperforms other state-of-the-art methods on these datasets. In addition, scMAE accurately identifies rare cell types, which are challenging to detect due to their low abundance. Furthermore, biological analyses confirm the biological significance of the identified cell subpopulations. AVAILABILITY AND IMPLEMENTATION: The source code of scMAE is available at: https://zenodo.org/records/10465991.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.