ArticleBriefings in bioinformatics2022
Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- Geneformer-guided multiomics integration identifies Pbx1 as a network hub of hematopoietic stem cell aging.Science advances · 2026Article
- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- A clustering method for single-cell RNA sequencing data based on denoising and masking learning.Frontiers in bioinformatics · 2026Article
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.BMC bioinformatics · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- Article
- scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.Genome biology · 2025Article
- scFTAT: a novel cell annotation method integrating FFT and transformer.BMC bioinformatics · 2025Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data.Scientific reports · 2024Article
- OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.Nature communications · 2024Article
- K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis.Computers in biology and medicine · 2024Article
- scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data.Interdisciplinary sciences, computational life sciences · 2024Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.
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What OpenQuestion holds
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.