Evidence map›Paper›PMID 42647174›Full record

ArticleBioinformatics (Oxford, England)2026

STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.

Kalen Clifton, Vivien Jiang, Rafael Dos Santos Peixoto, Srujan Singh, Ryo Matsuura, Hamid Rabb, Jean Fan

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Kalen CliftonCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.ORCID 0000-0002-4095-8104
Vivien JiangCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.ORCID 0009-0002-1417-4389
Rafael Dos Santos PeixotoCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.ORCID 0000-0002-7184-2868
Srujan SinghCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.ORCID 0000-0002-7498-4298
Ryo MatsuuraDivision of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID 0000-0003-4936-4852
Hamid RabbDivision of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID 0000-0002-4761-1103
Jean FanCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.ORCID 0000-0002-0212-5451

Funding

Comprehensive reference atlas construction, geolocation and data integration for HuBMAP HIVE [5 of 5]OT2OD033760 · OD · NEW YORK GENOME CENTER · PI SATIJA, RAHUL · 2022 to 2025
$6.1M
Computational methods for delineating subcellular and cellular spatial transcriptional heterogeneity along developmental trajectoriesR35GM142889 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI FAN, JEAN · 2021 to 2025
$2.0M
HuBMAP Integration, Visualization, and Engagement (HIVE) Initiative OT2-OD033760National Institute of General Medical Sciences of the National Institutes of Health R35-GM142889National Science Foundation CAREER-2047611NIGMS NIH HHS R35 GM142889NIH HHS OT2 OD033760
6 · The paper itself

Abstract

motivationComparative analysis of spatial transcriptomics (ST) data is needed to identify genes that spatially change in their expression patterns between conditions, such as in diseased versus healthy tissues. Existing methods generally fail to distinguish changes in spatial patterning by focusing only on changes in gene expression magnitude for methods adapted from non-spatial data or on changes in significance of spatial variability for methods focusing on spatially-resolved data.

resultsTo address these limitations, we develop STcompare, a statistical framework for comparative analysis of ST data by testing for differences in spatial correlation and spatial fold-change across structurally matched locations. Using simulated data, we demonstrate how STcompare provides distinct insights from bulk differential gene expression analysis and spatially variable gene expression analysis as well as other spatial comparison methods. STcompare further robustly controls for false positives even in the presence of spatial autocorrelation common in ST data. We apply STcompare to real ST data of biological replicates of mouse brains to confirm high spatial correspondence of gene expression patterns across samples. We apply STcompare to identify genes that spatially change in mouse kidneys with acute kidney injury compared to a healthy control, revealing tissue compartment-specific molecular dysregulation. Overall, the application of this spatially-aware comparative analysis will enable the discovery of differential spatially patterned genes across various physiological and technological axes of interest. AVAILABILITY: STcompare is implemented as an open-source R package at https://github.com/JEFworks-Lab/STcompare with additional documentation and tutorials available at https://jef.works/STcompare/.

Indexed as

Gene Expression ProfilingSoftwareSpatial TranscriptomicsTranscriptomeAlgorithmsAnimalsBrainMice

Identifiers

PMID42647174
PMCPMC13577157

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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.