ArticleCommunications biology2025
scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq.PLoS computational biology · 2026Article
- scDecorr: feature decorrelation based representation learning enables self-supervised alignment of multiple single-cell experiments.Scientific reports · 2026Article
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Revealing a coherent cell-state landscape across single-cell datasets with CONCORD.Nature biotechnology · 2026Article
- Revealing a coherent cell state landscape across single cell datasets with CONCORD.bioRxiv : the preprint server for biology · 2025Article
- scDCT: a conditional diffusion-based deep learning model for high-fidelity single-cell cross-modality translation.Briefings in bioinformatics · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.
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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.