{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:27:19Z","timestamp":1769880439687,"version":"3.49.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T00:00:00Z","timestamp":1570492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM122078"],"award-info":[{"award-number":["R01GM122078"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["3P50 CA196530"],"award-info":[{"award-number":["3P50 CA196530"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007431","name":"NRF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007431","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea government"},{"DOI":"10.13039\/501100003621","name":"MSIP","doi-asserted-by":"publisher","award":["NRF-2019R1C1C1003805"],"award-info":[{"award-number":["NRF-2019R1C1C1003805"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>A number of computational methods have been proposed recently to profile tumor microenvironment (TME) from bulk RNA data, and they have proved useful for understanding microenvironment differences among therapeutic response groups. However, these methods are not able to account for tumor proportion nor variable mRNA levels across cell types.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework for TME profiling that addresses these limitations. It is designed to provide robust estimates of tumor and immune cells proportions simultaneously, while accommodating mRNA level differences across cell types. Through comprehensive simulations and real data analyses, we demonstrate that NITUMID not only can accurately estimate tumor fractions and cell types\u2019 mRNA levels, which are currently unavailable in other methods; it also outperforms most existing deconvolution methods in regular cell type profiling accuracy. Moreover, we show that NITUMID can more effectively detect clinical and prognostic signals from gene expression profiles in tumor than other methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The algorithm is implemented in R. The source code can be downloaded at https:\/\/github.com\/tdw1221\/NITUMID.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz748","type":"journal-article","created":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T12:50:58Z","timestamp":1570107058000},"page":"1344-1350","source":"Crossref","is-referenced-by-count":26,"title":["NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6951-1189","authenticated-orcid":false,"given":"Daiwei","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health , New Haven, CT 06511, USA"}]},{"given":"Seyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Statistics, Sungkyunkwan University , Seoul 03063, South Korea"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health , New Haven, CT 06511, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,10,8]]},"reference":[{"key":"2023060910383919400_btz748-B1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1126\/science.aad0095","article-title":"Genomic correlates of response to ctla4 blockade in metastatic melanoma","volume":"350","author":"Allen","year":"2015","journal-title":"Science"},{"key":"2023060910383919400_btz748-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-015-0620-6","article-title":"Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy","volume":"16","author":"Angelova","year":"2015","journal-title":"Genome Biol"},{"key":"2023060910383919400_btz748-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-017-1349-1","article-title":"xcell: digitally portraying the tissue cellular heterogeneity landscape","volume":"18","author":"Aran","year":"2017","journal-title":"Genome Biol"},{"key":"2023060910383919400_btz748-B4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.csda.2006.11.006","article-title":"Algorithms and applications for approximate nonnegative matrix factorization","volume":"52","author":"Berry","year":"2007","journal-title":"Comput. 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