ArticleNucleic acids research2022
Integrated analysis of multimodal single-cell data with structural similarity.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed.
- Integration of Single-Cell and Bulk RNA Sequencing Data to Identify Lactylation-Related Gene Signatures in Hepatic Ischemia-Reperfusion Injury Using Machine Learning Algorithms.International journal of molecular sciences · 2026Article
- Cross-species insights from ART-D to uncover evolutionarily conserved oncogenic mechanisms.Molecular systems biology · 2026Article
- Single-cell transcriptomic unveils tumor-mediated reprogramming of neutrophils and their unique vulnerability that inhibits metastasis.Science advances · 2026Article
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- Integrating AI-driven single-cell analysis to decode epithelial heterogeneity: A prognostic signature and translational immunotherapy strategy targetingChinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Single‑cell mapping of neutrophil extracellular trap signatures in lung adenocarcinoma reveals immune landscapes, prognostic potential, and therapeutic targets.Translational oncology · 2026Article
- Single-cell analysis identifies a stemness-associated tumor cell subpopulation and develops a prognostic scoring model in esophageal squamous cell carcinoma.Translational oncology · 2026Article
- Identification and characterization of fibroblast-related biomarkers and pro-inflammatory subpopulations in periodontitis by integrated transcriptomic and single-cell analysis.Scientific reports · 2026Article
- Single-cell RNA-seq and machine learning identify HMGN2 as a lactylation-associated hub gene in heart failure.PloS one · 2026Article
- Combining multi-omics analysis methods to identify biomarkers for mitophagy involved in immune checkpoint inhibitors-related myocarditis.Frontiers in immunology · 2026Article
- PFKFB3 Mediated Glycolytic Reprogramming Drives Vascular Endothelial Injury Under Chronic Intermittent Hypoxia.International journal of biological sciences · 2026Article
- Metabolic reprogramming signature predicts immunotherapy efficacy in lung adenocarcinoma: TargetingChinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Article
- Developmental stage-dependent transcriptomic responses to neonatal intraventricular hemorrhage.Journal of neuroinflammation · 2025Article
- Machine learning identifies TIME subtypes linking EGFR mutations and immune states in lung adenocarcinoma.NPJ digital medicine · 2025Article
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- Casein kinase 1 family member CSNK1E can regulate proliferation and migration in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2025Article
- Differential phagocytosis induces diverse macrophage activation states in malignant gliomas.Journal for immunotherapy of cancer · 2025Article
- scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.Bioinformatics (Oxford, England) · 2025Article
- Integrative Single-Cell and Machine Learning Analysis Reveals Immune Microenvironment Remodelling in Lymph Node Metastasis of Lung Adenocarcinoma.Journal of cellular and molecular medicine · 2025Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios.
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