ArticleNucleic acids research2019
Cell lineage and communication network inference via optimization for single-cell transcriptomics.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.
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Who cites it
95 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies.Frontiers in neurology · 2024Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial transcriptomics.BMC bioinformatics · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Lignature provides a curated resource of ligand-induced transcriptomic signatures for signaling inference.Genome research · 2026Article
- Charting spatial ligand-target activity using Renoir.Nature communications · 2026Article
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.Bioinformatics (Oxford, England) · 2026Article
- Bridging cancer cell-intrinsic driver genes and -extrinsic cell-cell communication with Driver2Comm.PLoS computational biology · 2026Article
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation.bioRxiv : the preprint server for biology · 2025Article
- Sequential emergence and contraction of epithelial subtypes in the prenatal human choroid plexus revealed by a stem cell model.Nature communications · 2025Article
- Multi-Transcriptomic Analysis Reveals GSC-Driven MES-Like Differentiation via EMT in GBM Cell-Cell Communication.Biomedicines · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Cellular interactions within the immune microenvironment underpins resistance to cell cycle inhibition in breast cancers.Nature communications · 2025Article
- Mathematically mapping the network of cells in the tumor microenvironment.Cell reports methods · 2025Article
- CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.Nature protocols · 2025Article
35 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
The use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell-cell communication, a major remaining challenge. Here, we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell-cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.
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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.