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While tensor decomposition of such datasets has gained popularity, choosing a rank and trial for a decomposition is not straightforward. We show that taking into account the experimental data model can inspire the development of new metrics to assess the quality of a Non-negative CANDECOMP\/PARAFAC (NCPD) decomposition, and can thus be used to choose a rank and trial for the decomposition. Moreover, we show how framing the results via a dictionary learning framework can better enable interpretation of the components of the decomposition.<\/jats:p>","DOI":"10.64898\/2026.07.03.736447","type":"posted-content","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:40:19Z","timestamp":1783636819000},"source":"Crossref","is-referenced-by-count":0,"title":["Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4024-2058","authenticated-orcid":false,"given":"Anna","family":"Konstorum","sequence":"first","affiliation":[{"name":"Center for Computing Sciences, Institute for Defense Analyses, U.S.A."},{"name":"Department of Pathology, Yale School of Medicine, New Haven, Connecticut, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yale School of Medicine, New Haven, Connecticut, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1049-9795","authenticated-orcid":false,"given":"Shuchin","family":"Aeron","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tufts University, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4249-4742","authenticated-orcid":false,"given":"Misha","family":"Kilmer","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Tufts University, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4957-1544","authenticated-orcid":false,"given":"Steven H.","family":"Kleinstein","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yale School of Medicine, New Haven, Connecticut, U.S.A."},{"name":"Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, U.S.A."},{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, U.S.A."},{"name":"Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, Connecticut, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"62","key":"2026071304000506000_2026.07.03.736447v1.1","doi-asserted-by":"crossref","first-page":"eabf3733","DOI":"10.1126\/sciimmunol.abf3733","article-title":"Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study","volume":"6","year":"2021","journal-title":"Science immunology"},{"key":"2026071304000506000_2026.07.03.736447v1.2","unstructured":"Eun-Young K , Yan C , Hansi J D , Ramon LR , Michael S , Caroline K K , et al. 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