{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T14:10:07Z","timestamp":1751811007302,"version":"3.41.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T00:00:00Z","timestamp":1534982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HG008728","R01GM099409"],"award-info":[{"award-number":["R01HG008728","R01GM099409"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a recent technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. So far, no statistical framework to analyze MERFISH data is available.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present a Bayesian model for single cell transcript expression analysis on MERFISH data. We show that the model successfully captures uncertainty in MERFISH data and eliminates systematic biases that can occur in raw RNA molecule counts obtained with MERFISH. Our model accurately estimates transcript expression and additionally provides the full probability distribution and credible intervals for each transcript. We further show how this enables MERFISH to scale towards the whole genome while being able to control the uncertainty in obtained results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The presented model is implemented on top of Rust-Bio (K\u00f6ster, 2016) and available open-source as MERFISHtools (https:\/\/merfishtools.github.io). It can be easily installed via Bioconda (Gr\u00fcning et al., 2018). The entire analysis performed in this paper is provided as a fully reproducible Snakemake (K\u00f6ster and Rahmann, 2012) workflow via Zenodo (https:\/\/doi.org\/10.5281\/zenodo.752340).<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty718","type":"journal-article","created":{"date-parts":[[2018,8,22]],"date-time":"2018-08-22T19:19:20Z","timestamp":1534965560000},"page":"995-1001","source":"Crossref","is-referenced-by-count":5,"title":["A Bayesian model for single cell transcript expression analysis on MERFISH data"],"prefix":"10.1093","volume":"35","author":[{"given":"Johannes","family":"K\u00f6ster","sequence":"first","affiliation":[{"name":"Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany"},{"name":"Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA"},{"name":"Centrum Wiskunde and Informatica, Amsterdam, The Netherlands"}]},{"given":"Myles","family":"Brown","sequence":"additional","affiliation":[{"name":"Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA"},{"name":"Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA"},{"name":"Department of Medicine, Brigham and Women\u2019s Hospital and Harvard Medical School, Boston, MA, USA"}]},{"given":"X Shirley","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA"},{"name":"Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard T.H. 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