Evidence map›Paper›PMID 41928322›Full record

ArticleGenome medicine2026

Alignment of spatial transcriptomics slices across diseases, platforms and conditions.

Yu Wang, Zaiyi Liu, Qingchen Zang, Xiaoke Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Yu WangSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
Zaiyi LiuDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, Guangzhou, China.
Qingchen ZangSchool of Computer Science and Technology, Hainan University, No. 58 Renmin Avenue, Haikou, 570228, Hainan, China.
Xiaoke MaSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China. xkma@xidian.edu.cn.

Funding

Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University YJSJ25012Joint Funds of the National Natural Science Foundation of China U22A20345National Natural Science Foundation of China 62272361Natural Science Basic Research Program of Shaanxi 2025JC-QYCX-057Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0531100, 2024ZD0531103Xidian University Specially Funded Project for Interdisciplinary Exploration TZJHF202507
6 · The paper itself

Abstract

Spatial transcriptomics (ST) facilitates the exploration of biological tissue structures and functions within spatial context. Slice alignment and integration are prevalent for analyzing ST data, and current algorithms either focus on adjacent slices or require prior information to guide alignment, limits their applications for downstream analysis. Here, we present AlignDG, an information theory-based graph model that jointly aligns and integrates ST slices across diverse diseases, platforms and conditions without prior information. Experimental results demonstrate that AlignDG outperforms existing baselines in terms of precision, robustness, and efficiency with approximate 50% of slices, providing an effective alternative for analyzing ST data (code: https://github.com/xkmaxidian/AlignDG ).

Indexed as

DiseaseGene Expression ProfilingSpatial TranscriptomicsTranscriptomeAlgorithmsAnimalsComputational BiologyHumansSoftwareIntegrative analysisNetwork biologyOptimal transportSlice alignmentSpatial transcriptomics

Identifiers

PMID41928322
PMCPMC13169776

What OpenQuestion holds

Textmetadata
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.