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Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https:\/\/github.com\/xmuyulab\/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.<\/jats:p>","DOI":"10.1093\/bib\/bbac096","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T12:12:59Z","timestamp":1645791179000},"source":"Crossref","is-referenced-by-count":7,"title":["Application of individualized differential expression analysis in human cancer proteome"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6782-2197","authenticated-orcid":false,"given":"Yachen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, Fujian 316000, China"},{"name":"National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, 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Xiamen, Fujian 316000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China"},{"name":"State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuoqi","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8195-2027","authenticated-orcid":false,"given":"Xianlong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian 350122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengsha","family":"Tong","sequence":"additional","affiliation":[{"name":"National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China"},{"name":"State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2179-173X","authenticated-orcid":false,"given":"Rongshan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, Fujian 316000, China"},{"name":"National Institute for Data Science in Health 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