{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Dimmock S"],"funding":["Leverhulme Trust","Medical Research Council","Wellcome Trust","Biotechnology and Biological Sciences Research Council","Engineering and Physical Sciences Research Council"],"pagination":["RP102391"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12638045"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13"],"pubmed_abstract":["We can now collect cell-count data across whole animal brains quantifying recent neuronal activity, gene expression, or anatomical connectivity. This is a powerful approach since it is a multiregion measurement, but because the imaging is done postmortem, each animal only provides one set of counts. Experiments are expensive, and since cells are counted by imaging and aligning a large number of brain sections, they are time-intensive. The resulting datasets tend to be undersampled with fewer animals than brain regions. As a consequence, these data are a challenge for traditional statistical approaches. We present a 'standard' partially pooled Bayesian model for multiregion cell-count data and apply it to two example datasets. These examples demonstrate that hierarchical Bayesian methods ar"],"journal":["eLife"],"pubmed_title":["Hierarchical Bayesian modeling of multiregion brain cell count data."],"pmcid":["PMC12638045"],"funding_grant_id":["BB/L02134X/1","RF-2021-533","MR/S026630/1","206401/Z/17/Z","EP/W024020/1","BB/R007020/1","EP/R513179/1"],"pubmed_authors":["Houghton CJ","O'Donnell C","Schultz SR","Exley BMS","Menage L","Moore G","Warburton EC","Dimmock S","Delogu A"],"additional_accession":[]},"is_claimable":false,"name":"Hierarchical Bayesian modeling of multiregion brain cell count data.","description":"We can now collect cell-count data across whole animal brains quantifying recent neuronal activity, gene expression, or anatomical connectivity. This is a powerful approach since it is a multiregion measurement, but because the imaging is done postmortem, each animal only provides one set of counts. Experiments are expensive, and since cells are counted by imaging and aligning a large number of brain sections, they are time-intensive. The resulting datasets tend to be undersampled with fewer animals than brain regions. As a consequence, these data are a challenge for traditional statistical approaches. We present a 'standard' partially pooled Bayesian model for multiregion cell-count data and apply it to two example datasets. These examples demonstrate that hierarchical Bayesian methods ar","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T17:07:43.148Z","creation":"2026-05-19T03:12:03.578Z"},"accession":"S-EPMC12638045","cross_references":{"pubmed":["41269881"],"doi":["10.7554/eLife.102391"]}}