ArticleNature communications2023
scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Temporal single-cell transcriptional dynamics of murine pancreatic islet remodeling during hyperglycaemia progression.Molecular metabolism · 2026Article
- Robust semi-supervised scRNA-seq integration from virtual adversarial learning.bioRxiv : the preprint server for biology · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Charting spatial ligand-target activity using Renoir.Nature communications · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- ABaCo: addressing heterogeneity challenges in metagenomic data integration with adversarial generative models.Nucleic acids research · 2026Article
- Deep-Learning Tool ScVital Enables Species-Agnostic Integration of Cancer Cell States.Cancer research · 2026Article
- Benchmarking deep learning methods for biologically conserved single-cell integration.Genome biology · 2025Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Partially characterized topology guides reliable anchor-free scRNA-integration.Communications biology · 2025Article
- scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.Communications biology · 2025Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.bioRxiv : the preprint server for biology · 2024Article
- Batch correction of single-cell sequencing data via an autoencoder architecture.Bioinformatics advances · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.
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Registered trials
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