{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T15:41:07Z","timestamp":1775922067813,"version":"3.50.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["AL 2355\/1-1"],"award-info":[{"award-number":["AL 2355\/1-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Digital Tissue Deconvolution\u2014Aus Einzelzelldaten lernen","award":["420069742"],"award-info":[{"award-number":["420069742"]}]},{"name":"European Union\u2019s Horizon 2020","award":["754688"],"award-info":[{"award-number":["754688"]}]},{"DOI":"10.13039\/100016190","name":"Trond Mohn Stiftelse","doi-asserted-by":"publisher","award":["BFS2017TMT01"],"award-info":[{"award-number":["BFS2017TMT01"]}],"id":[{"id":"10.13039\/100016190","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Federal Ministry of Education and Research","award":["01ZX1912A"],"award-info":[{"award-number":["01ZX1912A"]}]},{"DOI":"10.13039\/501100002347","name":"BMBF","doi-asserted-by":"publisher","award":["01KD2208A"],"award-info":[{"award-number":["01KD2208A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"BMBF","doi-asserted-by":"publisher","award":["01KD2101C"],"award-info":[{"award-number":["01KD2101C"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"BMBF","doi-asserted-by":"publisher","award":["01KU1910A"],"award-info":[{"award-number":["01KU1910A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","award":["408885537"],"award-info":[{"award-number":["408885537"]}],"id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","award":["KFO5002"],"award-info":[{"award-number":["KFO5002"]}],"id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","award":["426671079"],"award-info":[{"award-number":["426671079"]}],"id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281\/zenodo.7548362).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae263","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T11:10:38Z","timestamp":1712920238000},"page":"i100-i109","source":"Crossref","is-referenced-by-count":7,"title":["Adaptive digital tissue deconvolution"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2015-5766","authenticated-orcid":false,"given":"Franziska","family":"G\u00f6rtler","sequence":"first","affiliation":[{"name":"Computational Biology Unit, Department of Biological Sciences, University of Bergen , N-5008 Bergen, Norway"},{"name":"Department of Oncology and Medical Physics, Haukeland University Hospital , 5021 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malte","family":"Mensching-Buhr","sequence":"additional","affiliation":[{"name":"Department of Medical Bioinformatics, University Medical Center G\u00f6ttingen , 37075 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00d8rjan","family":"Skaar","sequence":"additional","affiliation":[{"name":"Department of Informatics, Computational Biology Unit, University of Bergen , N-5008 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9936-3984","authenticated-orcid":false,"given":"Stefan","family":"Schrod","sequence":"additional","affiliation":[{"name":"Department of Medical Bioinformatics, University Medical Center G\u00f6ttingen , 37075 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Sterr","sequence":"additional","affiliation":[{"name":"Institute of Theoretical Physics, University of Regensburg , 93053 Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Institute of Theoretical Physics, University of Regensburg , 93053 Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6509-2143","authenticated-orcid":false,"given":"Tim","family":"Bei\u00dfbarth","sequence":"additional","affiliation":[{"name":"Department of Medical Bioinformatics, University Medical Center G\u00f6ttingen , 37075 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1184-3305","authenticated-orcid":false,"given":"Anagha","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Clinical Science, Computational Biology Unit, University of Bergen , N-5008 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3633-1330","authenticated-orcid":false,"given":"Helena U","family":"Zacharias","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School , 30625 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6410-1749","authenticated-orcid":false,"given":"Sushma Nagaraja","family":"Grellscheid","sequence":"additional","affiliation":[{"name":"Computational Biology Unit, Department of Biological Sciences, University of Bergen , N-5008 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1102-6532","authenticated-orcid":false,"given":"Michael","family":"Altenbuchinger","sequence":"additional","affiliation":[{"name":"Department of Medical Bioinformatics, University Medical Center G\u00f6ttingen , 37075 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"2024071814163628100_btae263-B1","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s10549-012-2405-x","article-title":"Ferritin stimulates breast cancer cells through an iron-independent mechanism and is localized within tumor-associated macrophages","volume":"137","author":"Alkhateeb","year":"2013","journal-title":"Breast Cancer Res Treat"},{"key":"2024071814163628100_btae263-B2","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1002\/msb.134947","article-title":"Digital cell quantification identifies global immune cell dynamics during influenza infection","volume":"10","author":"Altboum","year":"2014","journal-title":"Mol Syst Biol"},{"key":"2024071814163628100_btae263-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-19015-1","article-title":"Benchmarking of cell type deconvolution pipelines for transcriptomics data","volume":"11","author":"Avila Cobos","year":"2020","journal-title":"Nature Commun"},{"key":"2024071814163628100_btae263-B4","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1146\/annurev-immunol-042718-041728","article-title":"The myeloid cell compartment-cell by cell","volume":"37","author":"Bassler","year":"2019","journal-title":"Annu Rev Immunol"},{"key":"2024071814163628100_btae263-B5","doi-asserted-by":"crossref","first-page":"100219","DOI":"10.1016\/j.xcrm.2021.100219","article-title":"A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells","volume":"2","author":"Bhat-Nakshatri","year":"2021","journal-title":"Cell Rep Med"},{"key":"2024071814163628100_btae263-B6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/978-1-4939-7493-1_12","volume-title":"Cancer Systems Biology. Methods in Molecular Biology","author":"Chen","year":"2018"},{"key":"2024071814163628100_btae263-B7","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1093\/bib\/bbz166","article-title":"SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references","volume":"22","author":"Dong","year":"2021","journal-title":"Brief Bioinf"},{"key":"2024071814163628100_btae263-B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02362-7","article-title":"SpatialDWLS: accurate deconvolution of spatial transcriptomic data","volume":"22","author":"Dong","year":"2021","journal-title":"Genome Biol"},{"key":"2024071814163628100_btae263-B9","doi-asserted-by":"crossref","first-page":"5095","DOI":"10.1093\/bioinformatics\/btz444","article-title":"deconvSeq: deconvolution of cell mixture distribution in sequencing data","volume":"35","author":"Du","year":"2019","journal-title":"Bioinformatics"},{"key":"2024071814163628100_btae263-B10","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1089\/cmb.2019.0462","article-title":"Loss-function learning for digital tissue deconvolution","volume":"27","author":"G\u00f6rtler","year":"2020","journal-title":"J Comput Biol"},{"key":"2024071814163628100_btae263-B11","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2024071814163628100_btae263-B12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/00401706.1970.10488635","article-title":"Ridge regression: applications to nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"2024071814163628100_btae263-B13","first-page":"1","article-title":"Accurate estimation of cell composition in bulk expression through robust integration of single-cell information","volume":"11","author":"Jew","year":"2020","journal-title":"Nat Commun"},{"key":"2024071814163628100_btae263-B14","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1196\/annals.1415.005","article-title":"Localization of thymosin beta-4 in tumors","volume":"1112","author":"Larsson","year":"2007","journal-title":"Ann New York Acad Sci"},{"key":"2024071814163628100_btae263-B15","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1186\/1471-2407-14-750","article-title":"Characterization of \u03b22-microglobulin expression in different types of breast cancer","volume":"14","author":"Li","year":"2014","journal-title":"BMC Cancer"},{"key":"2024071814163628100_btae263-B16","doi-asserted-by":"crossref","first-page":"100440","DOI":"10.1016\/j.patter.2022.100440","article-title":"DAISM-DNNXMBD: highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks","volume":"3","author":"Lin","year":"2022","journal-title":"Patterns"},{"key":"2024071814163628100_btae263-B17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41587-022-01273-7","article-title":"Spatially informed cell-type deconvolution for spatial transcriptomics","volume":"40","author":"Ma","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024071814163628100_btae263-B18","doi-asserted-by":"crossref","first-page":"eaba2619","DOI":"10.1126\/sciadv.aba2619","article-title":"Deep learning\u2013based cell composition analysis from tissue expression profiles","volume":"6","author":"Menden","year":"2020","journal-title":"Sci Adv"},{"key":"2024071814163628100_btae263-B19","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/s41587-019-0114-2","article-title":"Determining cell type abundance and expression from bulk tissues with digital cytometry","volume":"37","author":"Newman","year":"2019","journal-title":"Nat Biotechn"},{"key":"2024071814163628100_btae263-B20","first-page":"8024","article-title":"PyTorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv Neural Inform Process Syst"},{"key":"2024071814163628100_btae263-B21","doi-asserted-by":"crossref","first-page":"e26476","DOI":"10.7554\/eLife.26476","article-title":"Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data","volume":"6","author":"Racle","year":"2017","journal-title":"Elife"},{"key":"2024071814163628100_btae263-B22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-02289-3","article-title":"Estimation of immune cell content in tumour tissue using single-cell RNA-seq data","volume":"8","author":"Schelker","year":"2017","journal-title":"Nat Commun"},{"key":"2024071814163628100_btae263-B23","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1089\/cmb.2019.0469","article-title":"DTD: an R package for digital tissue deconvolution","volume":"27","author":"Sch\u00f6n","year":"2020","journal-title":"J Comput Biol"},{"key":"2024071814163628100_btae263-B24","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc: Ser B (Methodol)"},{"key":"2024071814163628100_btae263-B25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-10802-z","article-title":"Accurate estimation of cell-type composition from gene expression data","volume":"10","author":"Tsoucas","year":"2019","journal-title":"Nat Commun"},{"key":"2024071814163628100_btae263-B26","first-page":"1","article-title":"Bulk tissue cell type deconvolution with multi-subject single-cell expression reference","volume":"10","author":"Wang","year":"2019","journal-title":"Nat Commun"},{"key":"2024071814163628100_btae263-B27","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/s41588-021-00911-1","article-title":"A single-cell and spatially resolved atlas of human breast cancers","volume":"53","author":"Wu","year":"2021","journal-title":"Nat Genet"},{"key":"2024071814163628100_btae263-B28","first-page":"3457","article-title":"Reduction in milk fat globule-EGF factor 8 inhibits triple-negative breast cancer cell viability and migration","volume":"17","author":"Yang","year":"2019","journal-title":"Oncol Lett"},{"key":"2024071814163628100_btae263-B29","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.trecan.2016.10.010","article-title":"B lymphocytes and cancer: a love-hate relationship","volume":"2","author":"Yuen","year":"2016","journal-title":"Trends Cancer"},{"key":"2024071814163628100_btae263-B30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13058-016-0785-2","article-title":"Thymosin beta 10 is a key regulator of tumorigenesis and metastasis and a novel serum marker in breast cancer","volume":"19","author":"Zhang","year":"2017","journal-title":"Breast Cancer Res"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/Supplement_1\/i100\/58585834\/btae263.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/Supplement_1\/i100\/58585834\/btae263.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T11:37:19Z","timestamp":1721302639000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/40\/Supplement_1\/i100\/7700906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":30,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2024,6,28]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae263","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.02.08.527583","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,6,28]]}}}