{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:55:28Z","timestamp":1781578528091,"version":"3.54.5"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDB38050100"],"award-info":[{"award-number":["XDB38050100"]}]},{"name":"Excellent Young Scientist Fund of Wuhan City","award":["21129040740"],"award-info":[{"award-number":["21129040740"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82101946"],"award-info":[{"award-number":["82101946"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Identifying co-expressed genes across tissue domains and cell types is essential for revealing co-functional genes involved in biological or pathological processes. While both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data offer insights into gene co-expression patterns, current methods typically utilize either data type alone, potentially diluting the co-functionality signals within co-expressed gene groups. To bridge this gap, we introduce muLtimodal co-Expressed GENes finDer (LEGEND), a novel computational method that integrates scRNA-seq and SRT data for identifying groups of co-expressed genes at both cell type and tissue domain levels. LEGEND employs an innovative hierarchical clustering algorithm designed to maximize intra-cluster redundancy and inter-cluster complementarity, effectively capturing more nuanced patterns of gene co-expression and spatial coherence. Enrichment and co-function analyses further showcase the biological relevance of these gene clusters and their utilities in exploring context-specific novel gene functions. Notably, LEGEND can reveal shifts in gene\u2013gene interactions under different conditions, providing insights into disease-associated gene crosstalk. Moreover, LEGEND can enhance the annotation accuracy of both spatial spots in SRT and single cells in scRNA-seq, and serve as a pioneering tool for identifying genes with designated spatial expression patterns. LEGEND is available at https:\/\/github.com\/ToryDeng\/LEGEND.<\/jats:p>","DOI":"10.1093\/gpbjnl\/qzaf056","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:42:35Z","timestamp":1751373755000},"source":"Crossref","is-referenced-by-count":8,"title":["LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-311X","authenticated-orcid":false,"given":"Tao","family":"Deng","sequence":"first","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) , Shenzhen 518172,","place":["China"]},{"name":"Shenzhen Research Institute of Big Data , Shenzhen 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