ArticleBriefings in bioinformatics2024
Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- scURL: uncertainty-sharpened representation learning for single-cell multi-omics clustering.Bioinformatics (Oxford, England) · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization.PLoS computational biology · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- Adaptive multi-view information bottleneck for multi-omics data clustering.Briefings in bioinformatics · 2026Article
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care.Journal of clinical medicine · 2025Review
- A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.Scientific reports · 2025Article
- FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.Briefings in bioinformatics · 2025Article
- Structure-guided integrative soft deep clustering analysis of scRNA-seq and scATAC-seq data.Frontiers in microbiology · 2025Article
- Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.Briefings in bioinformatics · 2024Article
- scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.Briefings in bioinformatics · 2024Article
- Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.Briefings in bioinformatics · 2024Article
- Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data.Briefings in bioinformatics · 2024Article
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
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6 authors.
Funding
Abstract
In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.
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