ArticleBioinformatics (Oxford, England)2023
CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.Bioinformatics (Oxford, England) · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Sparse dimensionality reduction for analyzing single-cell-resolved interactions.Bioinformatics advances · 2026Article
- MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics.PLoS computational biology · 2025Article
- GASTON-Mix: a unified model of spatial gradients and domains using spatial mixture-of-experts.Bioinformatics (Oxford, England) · 2025Article
- CellNEST reveals cell-cell relay networks using attention mechanisms on spatial transcriptomics.Nature methods · 2025Article
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- An algorithmic perspective on deciphering cell-cell interactions with spatial omics data.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Model Architecture Analysis and Implementation of TENET for Cell-Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data.Bio-protocol · 2025Article
- SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2025Article
- Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Foundation models in molecular biology.Biophysics reports · 2024Article
- A count-based model for delineating cell-cell interactions in spatial transcriptomics data.Bioinformatics (Oxford, England) · 2024Article
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3 authors.
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Abstract
motivationGene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.
resultsWe propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. AVAILABILITY AND IMPLEMENTATION: The source code and data is available at https://github.com/MihirBafna/CLARIFY.
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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.