ArticleNature communications2023
Batch alignment of single-cell transcriptomics data using deep metric learning.
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 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- Finetuning Foundation Models for Temporal Clinical Transcriptomics Data.Bioinformatics (Oxford, England) · 2026Article
- Cross-stage single-cell and spatial metabolome analyses reveal periderm specialization and tanshinone biosynthesis in Salvia miltiorrhiza roots.The New phytologist · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- scDecorr: feature decorrelation based representation learning enables self-supervised alignment of multiple single-cell experiments.Scientific reports · 2026Article
- Pathological mechanisms and treatment strategies for immune checkpoint inhibitor-associated myocarditis: insights from single-cell sequencing.Basic research in cardiology · 2026Review
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- scGPT: end-to-end protocol for fine-tuned retinal cell type annotation.Nature protocols · 2026Review
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- Benchmarking deep learning methods for biologically conserved single-cell integration.Genome biology · 2025Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- Unveiling tissue heterogeneity through genomic interaction-encoded image representation of RNA-sequencing data.American journal of human genetics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.
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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.