{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:33:53Z","timestamp":1767926033020,"version":"3.49.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Department of Scientific Computing"},{"DOI":"10.13039\/100007277","name":"Icahn School of Medicine at Mount Sinai","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007277","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute\u2019s Clinical Proteomic Tumor Analysis Consortium"},{"name":"CPTAC"},{"name":"National Institute of Health","award":["U24 CA210993"],"award-info":[{"award-number":["U24 CA210993"]}]},{"name":"National Institute of Health","award":["R01 GM108711"],"award-info":[{"award-number":["R01 GM108711"]}]},{"name":"National Institute of Health","award":["U01 CA214114"],"award-info":[{"award-number":["U01 CA214114"]}]},{"name":"National Institute of Health","award":["R01 CA189532"],"award-info":[{"award-number":["R01 CA189532"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R01EB021707"],"award-info":[{"award-number":["1R01EB021707"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMS-1148643"],"award-info":[{"award-number":["DMS-1148643"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene\/protein co-expression networks based on gene\/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>R codes can be found at https:\/\/github.com\/petraf01\/TSNet.<\/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\/bty280","type":"journal-article","created":{"date-parts":[[2018,4,17]],"date-time":"2018-04-17T06:38:49Z","timestamp":1523947129000},"page":"i528-i536","source":"Crossref","is-referenced-by-count":28,"title":["A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity"],"prefix":"10.1093","volume":"34","author":[{"given":"Francesca","family":"Petralia","sequence":"first","affiliation":[{"name":"Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Sema4, a Mount Sinai Venture, Stamford, CT, USA"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, Davis, Davis, CA, USA"}]},{"given":"Arthur","family":"Yan","sequence":"additional","affiliation":[{"name":"Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Sema4, a Mount Sinai Venture, Stamford, CT, USA"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[{"name":"Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051605120302900_bty280-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":"2023051605120302900_bty280-B2","doi-asserted-by":"crossref","DOI":"10.1038\/ncomms9971","article-title":"Systematic pan-cancer analysis of tumour purity","volume":"6","author":"Aran","year":"2015","journal-title":"Nat. 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