Evidence map›Paper›PMID 40033360›Full record

ArticleGenome medicine2025

STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes.

Ran Wang, Yan Qian, Xiaojing Guo, Fangda Song, Zhiqiang Xiong, Shirong Cai, Xiuwu Bian, Man Hon Wong, Qin Cao, Lixin Cheng and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

12 authors.

Ran WangCUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China.
Yan QianDepartment of Gastrointestinal Surgery Center, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 519082, China.
Xiaojing GuoHealth Data Science Center, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China.
Fangda SongSchool of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China.
Zhiqiang XiongCUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China.
Shirong CaiDepartment of Gastrointestinal Surgery Center, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 519082, China.
Xiuwu BianJinfeng Laboratory, Chongqing, 401329, China.
Man Hon WongDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China.
Qin CaoSchool of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China. qcao@cuhk.edu.hk.
Lixin ChengHealth Data Science Center, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China. easonlcheng@gmail.com.
Gang LuCUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China. lugang@cuhk.edu.hk.
Kwong Sak LeungDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China. ksleung@cse.cuhk.edu.hk.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515110759CUHK direct grant 2021.061National Natural Science Foundation of China 32100515National Natural Science Foundation of China 82173239Natural Science Foundation of Guangdong Province 2023A1515011187
6 · The paper itself

Abstract

Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

Indexed as

Computational BiologyGene Expression ProfilingSoftwareTranscriptomeBayes TheoremHumansNeoplasmsTumor MicroenvironmentBayesian modelSpatial expression componentsSpatially resolved transcriptomicsTissue module

Identifiers

PMID40033360
PMCPMC11874447

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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.