{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T20:26:41Z","timestamp":1768595201976,"version":"3.49.0"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China","award":["LHDMZ22H300002"],"award-info":[{"award-number":["LHDMZ22H300002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LDT23H19011H19"],"award-info":[{"award-number":["LDT23H19011H19"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971371"],"award-info":[{"award-number":["31971371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https:\/\/github.com\/zjupgx\/MATTE.<\/jats:p>","DOI":"10.1093\/bib\/bbad207","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T13:27:50Z","timestamp":1686317270000},"source":"Crossref","is-referenced-by-count":2,"title":["MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7645-0436","authenticated-orcid":false,"given":"Guoxin","family":"Cai","sequence":"first","affiliation":[{"name":"Zhejiang University Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, , Hangzhou 310058 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, , Hangzhou 310058 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2730-5483","authenticated-orcid":false,"given":"Zhan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, , Hangzhou 310058 , China"},{"name":"Zhejiang University School of Medicine The Fourth Affiliated Hospital, , Yiwu, 322000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Gu","sequence":"additional","affiliation":[{"name":"Iowa State University Department of Genetics, Development and Cell Biology, , Ames, IA 50011 , 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