ArticleBioinformatics (Oxford, England)2022
SpaceX: gene co-expression network estimation for spatial transcriptomics.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 29 citations in OpenAlex.
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co-expression modules.Quantitative biology (Beijing, China) · 2025Article
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Linking spatial omics to patient phenotypes at the population scale by BSNMani: Bayesian scalar-on-network regression with manifold learning.medRxiv : the preprint server for health sciences · 2025Article
- Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP.Genome research · 2025Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
- Spatially resolved gene signatures of white matter lesion progression in multiple sclerosis.Nature neuroscience · 2024Article
- Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.Briefings in bioinformatics · 2024Article
- Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.Statistics in biosciences · 2024Article
- Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning.Nature communications · 2024Article
- S100a9 might act as a modulator of the Toll-like receptor 4 transduction pathway in chronic rhinosinusitis with nasal polyps.Scientific reports · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationThe analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.
resultsWe develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. AVAILABILITY AND IMPLEMENTATION: The SpaceX R-package is available at github.com/bayesrx/SpaceX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.