{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:13:51Z","timestamp":1769825631515,"version":"3.49.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv\u2019s competitive statistical performance and superior computational efficiency.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>NLSDeconv is freely available at https:\/\/github.com\/tinachentc\/NLSDeconv as a Python package.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae747","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T18:01:25Z","timestamp":1734717685000},"source":"Crossref","is-referenced-by-count":4,"title":["NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics 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