ArticleGenome biology2024
Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- CCCdb: a comprehensive manually curated database for cell-cell communication in human and mouse.Nucleic acids research · 2026Article
- CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.Briefings in bioinformatics · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection.BMC biology · 2025Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.Genome biology · 2024Article
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Authors and funding
17 authors.
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Abstract
Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.
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Registered trials
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