{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:50:44Z","timestamp":1760953844008},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Accurate detection of differentially expressed genes between tumor and normal samples is a primary approach of cancer-related biomarker identification. Due to the infiltration of tumor surrounding normal cells, the expression data derived from tumor samples would always be contaminated with normal cells. Ignoring such cellular contamination would deflate the power of detecting DE genes and further confound the biological interpretation of the analysis results. For the time being, there does not exists any differential expression analysis approach for RNA-seq data in literature that can properly account for the contamination of tumor samples.<\/jats:p><jats:p>Results: Without appealing to any extra information, we develop a new method \u2018contamDE\u2019 based on a novel statistical model that associates RNA-seq expression levels with cell types. It is demonstrated through simulation studies that contamDE could be much more powerful than the existing methods that ignore the contamination. In the application to two cancer studies, contamDE uniquely found several potential therapy and prognostic biomarkers of prostate cancer and non-small cell lung cancer.<\/jats:p><jats:p>Availability and implementation: An R package contamDE is freely available at http:\/\/homepage.fudan.edu.cn\/zhangh\/softwares\/.<\/jats:p><jats:p>Contact: \u00a0zhanghfd@fudan.edu.cn<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv657","type":"journal-article","created":{"date-parts":[[2015,11,11]],"date-time":"2015-11-11T01:08:24Z","timestamp":1447204104000},"page":"705-712","source":"Crossref","is-referenced-by-count":13,"title":["contamDE: differential expression analysis of RNA-seq data for contaminated tumor samples"],"prefix":"10.1093","volume":"32","author":[{"given":"Qi","family":"Shen","sequence":"first","affiliation":[{"name":"1 State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, People\u2019s Republic of China and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"1 State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, People\u2019s Republic of China and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Jiang","sequence":"additional","affiliation":[{"name":"1 State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, People\u2019s Republic of China and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"1 State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, People\u2019s Republic of China and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zewei","family":"Luo","sequence":"additional","affiliation":[{"name":"2 School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"1 State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, People\u2019s Republic of China and"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,11,9]]},"reference":[{"key":"2023020110432454100_btv657-B1","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1093\/bioinformatics\/btt301","article-title":"DeMix: deconvolution for mixed cancer transcriptomes using raw measured data","volume":"29","author":"Ahn","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020110432454100_btv657-B2","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol."},{"key":"2023020110432454100_btv657-B3","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1038\/nprot.2013.099","article-title":"Count-based differential expression analysis of RNA sequencing data using r and bioconductor","volume":"8","author":"Anders","year":"2013","journal-title":"Nat. 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