ArticleNature biotechnology2021
Iterative single-cell multi-omic integration using online learning.
Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Article
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- LIGER2: Scalable Single-Cell Integration with On-Disk Datasets.bioRxiv : the preprint server for biology · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.Bioinformatics (Oxford, England) · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Orthogonal disentanglement of single-cell multi-omics reveals private and shared drivers of tissue development and pathogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- High-parameter spatial multi-omics through histology-anchored integration.Nature methods · 2026Article
- Research advancements in DNA methylation pertaining to ankylosing spondylitis.Frontiers in immunology · 2026Review
- Article
- multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.Briefings in bioinformatics · 2025Article
- Genetic integration with cell-specific nucleosome positioning resolves causal relationships underlying chromatin accessibility profiles.bioRxiv : the preprint server for biology · 2025Article
- Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.NPJ systems biology and applications · 2025Article
- JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization.BMC genomics · 2025Article
- CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.Cell genomics · 2025Article
8 more citing papers are in PubMed but not listed here.
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13 authors.
Funding
Abstract
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.
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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.