{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:46Z","timestamp":1772138086129,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T00:00:00Z","timestamp":1519689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002794","name":"Swedish Cancer Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017156","name":"Swedish e-Science Research Centre","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017156","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004815","name":"SERC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004815","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16\u00a0562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P&amp;lt;0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The ISOP method has been implemented as a R package and is available at https:\/\/github.com\/nghiavtr\/ISOP under a GPL-3 license.<\/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\/bty100","type":"journal-article","created":{"date-parts":[[2018,2,23]],"date-time":"2018-02-23T23:10:10Z","timestamp":1519427410000},"page":"2392-2400","source":"Crossref","is-referenced-by-count":13,"title":["Isoform-level gene expression patterns in single-cell RNA-sequencing data"],"prefix":"10.1093","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7945-5750","authenticated-orcid":false,"given":"Trung Nghia","family":"Vu","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quin F","family":"Wills","sequence":"additional","affiliation":[{"name":"Novo Nordisk Research Centre Oxford, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna R","family":"Kalari","sequence":"additional","affiliation":[{"name":"Department of Health Sciences Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nifang","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattias","family":"Rantalainen","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,2,27]]},"reference":[{"key":"2023012712593933300_bty100-B1","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.cell.2014.07.013","article-title":"Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis","volume":"158","author":"Aceto","year":"2014","journal-title":"Cell"},{"key":"2023012712593933300_bty100-B2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/nbt.3209","article-title":"High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin","volume":"33","author":"Achim","year":"2015","journal-title":"Nature Biotechnol"},{"key":"2023012712593933300_bty100-B3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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