ArticleGenome biology2020
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 1 of them a synthesis that pooled it.
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Who cites it
113 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
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis.PLoS computational biology · 2026Article
- Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization.PLoS computational biology · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- BARTsc identifies key transcriptional regulators from single-cell omics data.bioRxiv : the preprint server for biology · 2026Article
- Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN.Genome research · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- MOMHCA-SG: a multi-head cross-attention and similar graph convolutional network framework for Alzheimer's disease cell type classification.Frontiers in neuroscience · 2026Article
- Article
- LONMF: a non-negative matrix factorization model based on graph Laplacian and optimal transmission for paired single-cell multi-omics data integration.BMC bioinformatics · 2025Article
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes.Communications biology · 2025Article
- SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics.bioRxiv : the preprint server for biology · 2025Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.
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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.