2026-10-12T18:36:42Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33867327 2026-09-16T17:50:06Z category_272 category_45 category_734 category_931 category_811 category_134 portal_5 item_type_3 month_year_09_2026

<p>Parameter Configuration for Simulated Datasets.</p> Jinxia Wang (355966) Yuying Huo (24998904) Rui Zhao (43391) Yan Pan (5477) Jianqiang Wu (408814) Han Wang (254423) Xiangyu Li (1475548) Environmental Sciences not elsewhere classified Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Virology spatially resolved transcriptomic simultaneously providing multimodal resolution histology images opened new avenues interpreting tissue organization enabling accurate delineation deep learning method characterizing tissue architecture complementary information — spatial tissue domains spatial domain identification integrating spatial coordinates histological image features ulti -< b spamoal learns clustering mics graph contr div >< p omics molecular information spa </ b l </ b spatial information proteomic features molecular profiles </ b omics technologies omics datasets omics data spatial multi spamoal provides recent advances powerful framework human demonstrated friendly representations effective integration earning ), corresponding high <div><p>Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information—such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges, such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL (<b>Spa</b>tially <b>M</b>ulti-<b>O</b>mics graph contr<b>A</b>stive <b>L</b>earning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features, and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets from mouse and human demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.</p></div> 2026-09-16T17:41:23Z Dataset Dataset 10.1371/journal.pbio.3003690.s016 https://figshare.com/articles/dataset/_p_Parameter_Configuration_for_Simulated_Datasets_p_/33867327 CC BY 4.0