{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:10Z","timestamp":1772138050661,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"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":"National Institutes of Health","doi-asserted-by":"publisher","award":["HL111362-05A1"],"award-info":[{"award-number":["HL111362-05A1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["HL133932"],"award-info":[{"award-number":["HL133932"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NS115658-01"],"award-info":[{"award-number":["NS115658-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"US Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-18-1-0723 (BC171885P1"],"award-info":[{"award-number":["W81XWH-18-1-0723 (BC171885P1"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and \u21132,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer\u2019s disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R Scripts of swCAM are freely available at https:\/\/github.com\/Lululuella\/swCAM. A user\u2019s guide and a vignette are provided.<\/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\/btab839","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T07:27:24Z","timestamp":1639121244000},"page":"1403-1410","source":"Crossref","is-referenced-by-count":10,"title":["swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution"],"prefix":"10.1093","volume":"38","author":[{"given":"Lulu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Arlington, VA 22203, USA"}]},{"given":"Chiung-Ting","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Arlington, VA 22203, USA"}]},{"given":"Chia-Hsiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-3295","authenticated-orcid":false,"given":"Rujia","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, SUNY Upstate Medical University , Syracuse, NY 13210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5986-4415","authenticated-orcid":false,"given":"Chunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, SUNY Upstate Medical University , Syracuse, NY 13210, USA"}]},{"given":"Robert","family":"Clarke","sequence":"additional","affiliation":[{"name":"The Hormel Institute, University of Minnesota , Austin, MN 55912, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6743-7413","authenticated-orcid":false,"given":"Guoqiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Arlington, VA 22203, USA"}]},{"given":"Jennifer E","family":"Van Eyk","sequence":"additional","affiliation":[{"name":"Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center , Los Angeles, CA 90048, USA"}]},{"given":"David M","family":"Herrington","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Wake Forest University , Winston-Salem, NC 27157, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-1102","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Arlington, VA 22203, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"2023020201174437000_btab839-B1","first-page":"1969","article-title":"Computational deconvolution of transcriptomics data from mixed cell populations","volume":"34","author":"Avila Cobos","year":"2018","journal-title":"Bioinformatics (Oxford, England)"},{"key":"2023020201174437000_btab839-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2010","journal-title":"Found. 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