{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:13Z","timestamp":1740185113666,"version":"3.37.3"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All steps of our analysis were implemented in annotated Python notebooks (https:\/\/github.com\/paulfogel\/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install.<\/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\/btab773","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T20:18:03Z","timestamp":1636489083000},"page":"1015-1021","source":"Crossref","is-referenced-by-count":1,"title":["A2Sign: Agnostic Algorithms for Signatures\u2014a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8829-022X","authenticated-orcid":false,"given":"Galina","family":"Boldina","sequence":"first","affiliation":[{"name":"Sanofi, R&D Translational Sciences France, Bioinformatics , Sanofi, F-91385 Chilly-Mazarin Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Fogel","sequence":"additional","affiliation":[{"name":"Consultant , F-75006 Paris, France"},{"name":"Advestis , F-75008 Paris, France"},{"name":"Quinten , F-75017 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corinne","family":"Rocher","sequence":"additional","affiliation":[{"name":"Sanofi, R&D Translational Sciences France, Bioinformatics , Sanofi, F-91385 Chilly-Mazarin Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Bettembourg","sequence":"additional","affiliation":[{"name":"Sanofi, R&D Translational Sciences France, Bioinformatics , Sanofi, F-91385 Chilly-Mazarin Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Luta","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University , Washington, DC 20057, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Franck","family":"Aug\u00e9","sequence":"additional","affiliation":[{"name":"Sanofi, R&D Translational Sciences France, Bioinformatics , Sanofi, F-91385 Chilly-Mazarin Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"2023020108525289100_btab773-B1","doi-asserted-by":"crossref","first-page":"e6098","DOI":"10.1371\/journal.pone.0006098","article-title":"Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus","volume":"4","author":"Abbas","year":"2009","journal-title":"PLoS One"},{"key":"2023020108525289100_btab773-B2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1186\/s13059-016-1070-5","article-title":"Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression","volume":"17","author":"Becht","year":"2016","journal-title":"Genome Biol"},{"key":"2023020108525289100_btab773-B3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/0471725153","volume-title":"Regression Diagnostics: Identifying Influential Data and Sources of Collinearity","author":"Belsley","year":"1980"},{"key":"2023020108525289100_btab773-B4","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1038\/s41588-019-0505-9","article-title":"Landscape of stimulation-responsive chromatin across diverse human immune cells","volume":"51","author":"Calderon","year":"2019","journal-title":"Nat. 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