ArticleNature communications2024
Modal-nexus auto-encoder for multi-modality cellular data integration and imputation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 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
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.Bioinformatics (Oxford, England) · 2026Article
- Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.Nature communications · 2026Article
- A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.Nature communications · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?Briefings in bioinformatics · 2026Article
- Article
- scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping.bioRxiv : the preprint server for biology · 2025Article
- Systematically Revealing Quantitative Multi-Target Integrative Effects of Plants With Artificial Intelligence Method.Plant biotechnology journal · 2025Article
- A computational framework to dissect imputation strategies for single-cell histone modification data.NAR genomics and bioinformatics · 2025Article
- ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics.bioRxiv : the preprint server for biology · 2025Article
- Cell Decoder: decoding cell identity with multi-scale explainable deep learning.Genome biology · 2025Article
- Multi-omics single-cell data alignment and integration with enhanced contrastive learning and differential attention mechanism.Bioinformatics (Oxford, England) · 2025Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there's a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E's accuracy and robustness in multi-modality cellular data integration and imputation.
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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.