ArticleBioinformatics (Oxford, England)2023
scAce: an adaptive embedding and clustering method for single-cell gene expression data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Annotation-free phenotype prediction using knowledge-augmented clustering from single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- scSCC: A swapped contrastive learning-based clustering method for single-cell gene expression data.Quantitative biology (Beijing, China) · 2025Article
- Artificial variables help to avoid over-clustering in single-cell RNA sequencing.American journal of human genetics · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationSince the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment.
resultsIn this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. AVAILABILITY AND IMPLEMENTATION: The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.
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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.