{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:06Z","timestamp":1772138046869,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Programma per Giovani Ricercatori Rita Levi Montalcini"},{"name":"Italian Ministry of Education"},{"name":"University and Research and by the National Cancer Institute of the National Institutes of Health","award":["2U24CA180996"],"award-info":[{"award-number":["2U24CA180996"]}]},{"DOI":"10.13039\/501100005010","name":"Italian Association for Cancer Research","doi-asserted-by":"crossref","award":["IG 21837"],"award-info":[{"award-number":["IG 21837"]}],"id":[{"id":"10.13039\/501100005010","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Giovanni Armenise-Harvard Foundation and ERC Starting Grant","award":["CZF2019-002443"],"award-info":[{"award-number":["CZF2019-002443"]}]},{"name":"Chan Zuckerberg Initiative DAF"},{"DOI":"10.13039\/100000923","name":"Silicon Valley Community Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute of the National Institutes of Health","award":["2U24CA180996"],"award-info":[{"award-number":["2U24CA180996"]}]},{"DOI":"10.13039\/501100005010","name":"Italian Association for Cancer Research","doi-asserted-by":"crossref","award":["IG21837"],"award-info":[{"award-number":["IG21837"]}],"id":[{"id":"10.13039\/501100005010","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PsiNorm is implemented in the scone Bioconductor package and available at https:\/\/bioconductor.org\/packages\/scone\/.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab641","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T07:46:10Z","timestamp":1631087170000},"page":"164-172","source":"Crossref","is-referenced-by-count":14,"title":["PsiNorm: a scalable normalization for single-cell RNA-seq data"],"prefix":"10.1093","volume":"38","author":[{"given":"Matteo","family":"Borella","sequence":"first","affiliation":[{"name":"Department of Biology, University of Padova , Padua 35121, Italy"}]},{"given":"Graziano","family":"Martello","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Padova , Padua 35121, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-5012","authenticated-orcid":false,"given":"Davide","family":"Risso","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Padova , Padua 35121, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4792-9047","authenticated-orcid":false,"given":"Chiara","family":"Romualdi","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Padova , Padua 35121, Italy"}]}],"member":"286","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"2023020108410408400_btab641-B1","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023020108410408400_btab641-B2","first-page":"1","author":"Arnold","year":"2015"},{"key":"2023020108410408400_btab641-B3","doi-asserted-by":"crossref","first-page":"8339","DOI":"10.1038\/s41598-018-26735-4","article-title":"Broad distribution spectrum from Gaussian to power law appears in stochastic variations in RNA-seq data","volume":"8","author":"Awazu","year":"2018","journal-title":"Sci. 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