ArticleiScience2023
SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 31 citations in OpenAlex.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Key regulatory elements of the TGFβ-LRRC15 axis predict disease progression and immunotherapy resistance across cancer types.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- ROS-Centered Transcriptomic Regulatory Networks Linking Salinity Stress, Antioxidant Defense and Processability Traits inCurrent issues in molecular biology · 2026Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Neuronal and glial networks interact with traumatic brain injury to modulate cognition in ABCD study.NPJ systems biology and applications · 2026Article
- Estradiol treatment induces both shared and unique gene regulation and networks in adipose cell types of gonadectomized obese XX and XY mice.Biology of sex differences · 2026Article
- Computational integration of in vivo single cell and in vitro bulk transcriptomics across 236 human and mouse datasets differentiates physiological versus non-physiological hepatic cell lines for hepatotoxicity screening.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Inferring cell-type-specific gene regulatory network from cellular transcriptomics data with GeneLink.Briefings in bioinformatics · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Key Connectomes and Synaptic-Compartment-Specific Risk Genes Drive Pathological α-Synuclein Spreading.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- scPRINT: pre-training on 50 million cells allows robust gene network predictions.Nature communications · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.
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Registered trials
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.