ArticleBioinformatics (Oxford, England)2021
Identifying signaling genes in spatial single-cell expression data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 56 citations in OpenAlex.
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- A unified analysis of atlas single-cell data.Genome research · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Spatial Cellular Networks from omics data with SpaCeNet.Genome research · 2024Article
- Intracellular spatial transcriptomic analysis toolkit (InSTAnT).Nature communications · 2024Article
- Cell-cell communication: new insights and clinical implications.Signal transduction and targeted therapy · 2024Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.Molecular systems biology · 2023Article
- SCS: cell segmentation for high-resolution spatial transcriptomics.Nature methods · 2023Article
- Review
- SCS: cell segmentation for high-resolution spatial transcriptomics.bioRxiv : the preprint server for biology · 2023Article
- Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.Cell systems · 2023Article
- Article
- SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics.Genome biology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationRecent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact.
resultsWe developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. AVAILABILITY AND IMPLEMENTATION: MESSI is available at: https://github.com/doraadong/MESSI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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