Evidence map›Paper›PMID 39127044›Full record

ArticleCell reports methods2024

Discovery and generalization of tissue structures from spatial omics data.

Zhenqin Wu, Ayano Kondo, Monee McGrady, Ethan A G Baker, Benjamin Chidester, Eric Wu, Maha K Rahim, Nathan A Bracey, Vivek Charu, Raymond J Cho and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
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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

15 authors.

Zhenqin WuEnable Medicine, Menlo Park, CA 94025, USA. Electronic address: zhenqin@enablemedicine.com.
Ayano KondoEnable Medicine, Menlo Park, CA 94025, USA.
Monee McGradyEnable Medicine, Menlo Park, CA 94025, USA.
Ethan A G BakerEnable Medicine, Menlo Park, CA 94025, USA.
Benjamin ChidesterEnable Medicine, Menlo Park, CA 94025, USA.
Eric WuEnable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Maha K RahimEnable Medicine, Menlo Park, CA 94025, USA.
Nathan A BraceyInstitute of Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA.
Vivek CharuDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Raymond J ChoDepartment of Dermatology, University of California, San Francisco, San Francisco, CA, USA.
Jeffrey B ChengDepartment of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Department of Dermatology, Veterans Affairs Medical Center, San Francisco, CA, USA.
Maryam AfkarianDivision of Nephrology, Department of Medicine, University of California, Davis, Davis, CA 95618, USA.
James ZouEnable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA. Electronic address: jamesz@stanford.edu.
Aaron T MayerEnable Medicine, Menlo Park, CA 94025, USA. Electronic address: aaron@enablemedicine.com.
Alexandro E TrevinoEnable Medicine, Menlo Park, CA 94025, USA. Electronic address: alex@enablemedicine.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

Indexed as

Computational BiologyAnimalsDiabetic NephropathiesHumansMiceSkin Diseasesartificial intelligenceCP: Systems biologyspatial omicsunsupervised annotation

Identifiers

PMID39127044
PMCPMC11384092

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.