{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T22:18:50Z","timestamp":1785363530415,"version":"3.55.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020"},{"name":"Marie Sklodowska-Curie","award":["801133"],"award-info":[{"award-number":["801133"]}]},{"DOI":"10.13039\/501100005416","name":"Norwegian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Oslo through the Centre for Molecular Medicine Norway (NCMM","award":["187615"],"award-info":[{"award-number":["187615"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes\u2019 unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>rPanglaoDB is implemented as an R package available through the CRAN repositories https:\/\/CRAN.R-project.org\/package=rPanglaoDB.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab549","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T15:13:02Z","timestamp":1627312382000},"page":"580-582","source":"Crossref","is-referenced-by-count":17,"title":["rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database"],"prefix":"10.1093","volume":"38","author":[{"given":"Daniel","family":"Osorio","sequence":"first","affiliation":[{"name":"Centre for Molecular Medicine Norway (NCMM), University of Oslo , 0349 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-3130","authenticated-orcid":false,"given":"Marieke L","family":"Kuijjer","sequence":"additional","affiliation":[{"name":"Centre for Molecular Medicine Norway (NCMM), University of Oslo , 0349 Oslo, Norway"},{"name":"Department of Pathology, Leiden University Medical Center , Leiden, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8081-6725","authenticated-orcid":false,"given":"James J","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Veterinary Integrative Biosciences, Texas A&M University , College Station, TX 77843, USA"},{"name":"Department of Electrical & Computer Engineering, Texas A&M University , College Station, TX 77843, USA"},{"name":"Interdisciplinary Program of Genetics, Texas A&M University , College Station, TX 77843, USA"},{"name":"Center for Statistical Bioinformatics, Texas A&M University , College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"2023020108412900400_btab549-B1","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1093\/bioinformatics\/btab003","article-title":"Nebulosa recovers single cell gene expression signals by kernel density estimation","volume":"37","author":"Alquicira-Hernandez","year":"2021","journal-title":"Bioinformatics"},{"key":"2023020108412900400_btab549-B2","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. 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