{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Andrade Barbosa B"],"funding":["stichting Cancer Center Amsterdam"],"pubmed_abstract":["Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustne"],"journal":["Nature communications"],"pagination":["6106"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8528834"],"repository":["biostudies-literature"],"pubmed_title":["Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data."],"pmcid":["PMC8528834"],"pubmed_authors":["van Asten SD","Andrade Barbosa B","van de Wiel MA","Garcia Vallejo JJ","Farina-Sarasqueta A","Verheij J","van Laarhoven HWM","Kim Y","Ylstra B","Oh JW","Dijk F"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data.","description":"Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustne","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-05-08T11:16:48.079Z","creation":"2025-05-29T20:24:12.808Z"},"accession":"S-EPMC8528834","cross_references":{"pubmed":["34671028"],"doi":["10.1038/s41467-021-26328-2"]}}