ArticleNature communications2021
Multi-domain translation between single-cell imaging and sequencing data using autoencoders.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.
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Who cites it
95 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
- A pre-trained large generative model for translating single-cell transcriptomes to proteomes.Nature biomedical engineering · 2026Article
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- T2Pdecoder enables protein-centric analyses from transcriptomic data.Nature communications · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Article
- Detecting chromatin state alterations in PBMCs associated with Type 2 Diabetes Mellitus.Communications medicine · 2026Article
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond.Nature computational science · 2026Article
- A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning.NPJ digital medicine · 2025Article
- scKGBERT: a knowledge-enhanced foundation model for single-cell transcriptomics.Genome biology · 2025Article
- sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.bioRxiv : the preprint server for biology · 2025Article
- HILAMA: High-dimensional multi-omics mediation analysis with latent confounding.BMC medical research methodology · 2025Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
35 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.
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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.