{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nozari Z"],"funding":["European Union’s Horizon 2020 research and innovation program under","German Federal Ministry of Education and Research","European Union's Horizon 2020 research and innovation program under"],"pagination":["btaf455"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12448907"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(9)"],"pubmed_abstract":["<h4>Motivation</h4>The cellular composition of a solid tissue can be assessed either through the physical dissociation of the tissue followed by single-cell analysis techniques or by computational deconvolution of bulk gene expression profiles. However, both approaches are prone to significant biases. Tissue dissociation often results in disproportionate cell loss, while deconvolution is hindered by biological and technological inconsistencies between the datasets it relies on.<h4>Results</h4>Using calibration datasets that include both experimentally measured and deconvolution-based cell compositions, we present a new method, Harp, which reconciles these approaches to produce more reliable deconvolution results in applications where only gene expression data is available. Both on simulate"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Harp: data harmonization for computational tissue deconvolution across diverse transcriptomics platforms."],"pmcid":["PMC12448907"],"funding_grant_id":["031L0173"],"pubmed_authors":["Spang R","Hutchinson JA","Nozari Z","Schon M","Simeth J","Huttl P"],"additional_accession":[]},"is_claimable":false,"name":"Harp: data harmonization for computational tissue deconvolution across diverse transcriptomics platforms.","description":"<h4>Motivation</h4>The cellular composition of a solid tissue can be assessed either through the physical dissociation of the tissue followed by single-cell analysis techniques or by computational deconvolution of bulk gene expression profiles. However, both approaches are prone to significant biases. Tissue dissociation often results in disproportionate cell loss, while deconvolution is hindered by biological and technological inconsistencies between the datasets it relies on.<h4>Results</h4>Using calibration datasets that include both experimentally measured and deconvolution-based cell compositions, we present a new method, Harp, which reconciles these approaches to produce more reliable deconvolution results in applications where only gene expression data is available. Both on simulate","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T15:40:20.952Z","creation":"2026-04-29T03:12:41.092Z"},"accession":"S-EPMC12448907","cross_references":{"pubmed":["40857392"],"doi":["10.1093/bioinformatics/btaf455"]}}