ArticleBMC bioinformatics2022
scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- A Hybrid Framework for Red Blood Cell Labeling Using Elliptical Fitting, Autoencoding, and Data Augmentation.Journal of imaging · 2025Article
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.Scientific reports · 2025Article
- Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.Journal of chemical information and modeling · 2025Article
- The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.Nature communications · 2024Article
- scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.Bioinformatics (Oxford, England) · 2024Article
- Semi-supervised integration of single-cell transcriptomics data.Nature communications · 2024Article
- scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.BMC bioinformatics · 2023Article
- Quantum graph embedding of transcription factor-gene networks reveals key modules in periodontal bone inflammation: Comparative analysis of GAE and GAN.Journal of oral biology and craniofacial researchArticle
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing (scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach.
resultsIn this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis.
conclusionsExperiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.
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