ArticleNAR genomics and bioinformatics2020
scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- ScEnsemble: weighted hypergraph ensemble clustering for single-cell RNA sequencing.BMC bioinformatics · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
- Robust self-supervised machine learning for single cell embeddings and annotations.bioRxiv : the preprint server for biology · 2025Article
- Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2025Article
- The paradigm shift in neural stem cells basic research driven by artificial intelligence related technologies.Frontiers in cellular neuroscience · 2025Review
- Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2024Article
- ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.Genome biology · 2024Article
- scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.Nature communications · 2024Article
- scCross: efficient search for rare subpopulations across multiple single-cell samples.Bioinformatics (Oxford, England) · 2024Article
- Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.Nature communications · 2024Article
- Investigation of the mixed origins of the MGC-803 cell line reveals that it is a hybrid cell line derived from HeLa.Human cell · 2024Article
- Rarity: discovering rare cell populations from single-cell imaging data.Bioinformatics (Oxford, England) · 2023Article
- Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.Briefings in bioinformatics · 2023Article
- Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.Briefings in bioinformatics · 2023Article
- Statistical and machine learning methods for immunoprofiling based on single-cell data.Human vaccines & immunotherapeutics · 2023Review
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
- ProgClust: A progressive clustering method to identify cell populations.Frontiers in genetics · 2023Article
- Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based
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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.