ArticleCell reports methods2023
Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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Who cites it
48 citing papers in PubMed, 92 citations in OpenAlex.
- Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.NAR genomics and bioinformatics · 2026Article
- SpaMFG: a spatial multi-omics integration method based on feature grouping.Bioinformatics (Oxford, England) · 2026Article
- scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.PLoS computational biology · 2026Article
- Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering.Interdisciplinary sciences, computational life sciences · 2026Article
- Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.iScience · 2026Article
- TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.Briefings in bioinformatics · 2026Article
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- GP-DHT: A dual-head transformer with supervised contrastive learning for shared-gene pretraining and single-cell GRN prediction.PloS one · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.Advances in neural information processing systems · 2025Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive.Briefings in bioinformatics · 2025Article
- Deep clustering of single-cell RNA-seq using adversarial graph contrastive learning.Briefings in bioinformatics · 2025Article
- scGGC: a two-stage strategy for single-cell clustering through cellular gene pathway construction.Briefings in bioinformatics · 2025Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.Scientific reports · 2025Article
- Inferring gene regulatory networks by hypergraph generative model.Cell reports methods · 2025Article
- scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.Genome biology · 2025Article
- lncRNA localization and feature interpretability analysis.Molecular therapy. Nucleic acids · 2025Article
- Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
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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.