ArticleBriefings in bioinformatics2023
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study.Frontiers in endocrinology · 2026Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- scHDeepInsight: a hierarchical deep learning framework for precise immune cell annotation in single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- An overview of computational methods in single-cell transcriptomic cell type annotation.Briefings in bioinformatics · 2025Review
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- sTPLS: identifying common and specific correlated patterns under multiple biological conditions.Briefings in bioinformatics · 2025Article
- Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.PLoS genetics · 2025Article
- WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.Frontiers in genetics · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Interpreting single-cell and spatial omics data using deep neural network training dynamics.Nature computational science · 2024Article
- scGO: interpretable deep neural network for cell status annotation and disease diagnosis.Briefings in bioinformatics · 2024Article
- Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.Briefings in bioinformatics · 2024Article
- Advances in AI and machine learning for predictive medicine.Journal of human genetics · 2024Review
- A self-training interpretable cell type annotation framework using specific marker gene.Bioinformatics (Oxford, England) · 2024Article
- scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.Briefings in bioinformatics · 2024Article
- Enhanced analysis of tabular data through Multi-representation DeepInsight.Scientific reports · 2024Article
- CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data.Briefings in bioinformatics · 2023Article
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5 authors.
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Abstract
Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further. To effectively solve this issue, we propose a novel supervised annotation method, scDeepInsight. The scDeepInsight method is capable of performing manifold assignments. It is competent in executing data integration through batch normalization, performing supervised training on the reference dataset, doing outlier detection and annotating cell-types on query datasets. Moreover, it can help identify active genes or marker genes related to cell-types. The training of the scDeepInsight model is performed in a unique way. Tabular scRNA-seq data are first converted to corresponding images through the DeepInsight methodology. DeepInsight can create a trainable image transformer to convert non-image RNA data to images by comprehensively comparing interrelationships among multiple genes. Subsequently, the converted images are fed into convolutional neural networks such as EfficientNet-b3. This enables automatic feature extraction to identify the cell-types of scRNA-seq samples. We benchmarked scDeepInsight with six other mainstream cell annotation methods. The average accuracy rate of scDeepInsight reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods.
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