{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:10:17Z","timestamp":1770977417315,"version":"3.50.1"},"reference-count":141,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data\u2014which has higher dimensionality, weaker signals and more complex distributional properties\u2014is much more challenging. Developments in the literature are often \u2018scattered\u2019, with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the \u2018overall framework\u2019 of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss \u2018special topics\u2019 including interaction analysis, multi-datasets analysis and multi-omics analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbab585","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T20:11:14Z","timestamp":1640117474000},"source":"Crossref","is-referenced-by-count":23,"title":["Analysis of cancer omics data: a selective review of statistical techniques"],"prefix":"10.1093","volume":"23","author":[{"given":"Chenjin","family":"Ma","sequence":"first","affiliation":[{"name":"College of Statistics and Data Science, Faculty of Science, Beijing University of Technology, Beijing, China"}]},{"given":"Mengyun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4999","authenticated-orcid":false,"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"2022031506301778800_ref1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jprot.2017.08.010","article-title":"Clinical multi-omics strategies for the effective cancer management","volume":"188","author":"Yoo","year":"2018","journal-title":"J Proteomics"},{"key":"2022031506301778800_ref2","doi-asserted-by":"crossref","first-page":"9836256","DOI":"10.1155\/2018\/9836256","article-title":"Onco-multi-OMICS approach: a new frontier in cancer research","volume":"2018","author":"Chakraborty","year":"2018","journal-title":"Biomed Res 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