Evidence map›Paper›PMID 40964272›Full record

ArticlebioRxiv : the preprint server for biology2025

S3R: Modeling spatially varying associations with Spatially Smooth Sparse Regression.

Xinyu Zhou, Pengtao Dang, Xiao Wang, Laura Xianlu Peng, Jen Jen Yeh, Nan Zhang, Brian Neelon, Rosie Sears, Teresa Zimmers, Chi Zhang and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xinyu ZhouDepartment of Computer Science, Indiana University, Bloomington, IN, 46202, USA.
Pengtao DangDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97202, USA.
Xiao WangDepartment of Computer Science, Indiana University, Bloomington, IN, 46202, USA.
Laura Xianlu PengDepartments of Surgery and Pharmacology, University of North Carolina, Chapel Hill, NC, 27514, USA.
Jen Jen YehDepartments of Surgery and Pharmacology, University of North Carolina, Chapel Hill, NC, 27514, USA.
Nan ZhangSchool of Data Science, Fudan University, Shanghai, 200433, China.
Brian NeelonDepartments of Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.
Rosie SearsDept. of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, 97202, USA.ORCID 0000-0003-1558-2413
Teresa ZimmersBrenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR, 97202, USA.ORCID 0000-0001-7872-0540
Chi ZhangCenter for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Sha CaoCenter for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.

Funding

Advancing computational modeling of disease metabolism by integrating AI and systems biologyR35GM155028 · NIGMS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Sha Cao · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM155028
6 · The paper itself

Abstract

Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that estimates location-specific coefficients linking a response feature to high-dimensional spatial predictors. S3R unites structured sparsity with a minimum-spanning-tree-guided smoothness penalty, yielding coefficient fields that are coherent within neighborhoods yet permit sharp boundaries. In synthetic data, S3R accurately recovers spatially varying effects, selects relevant predictors, and preserves known boundaries. Applied to Visium-based ST data, S3R recapitulates layer-specific target-TF associations in human dorsolateral prefrontal cortex with concordant layer-wise correlations in matched single-cell data. In acute

Indexed as

cross–cell type co-variationsparse regressionspatial interactionsspatially variable relationshipsspatial transcriptomics

Identifiers

PMID40964272
PMCPMC12440005

What OpenQuestion holds

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Registered trials

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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.