{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["34(14)"],"submitter":["Vu TN"],"funding":["SERC","Swedish Cancer Society","Swedish e-Science Research Centre","Swedish Research Council"],"pubmed_abstract":["Motivation: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. Results: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, "],"journal":["Bioinformatics (Oxford, England)"],"pagination":["2392-2400"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6041805"],"repository":["biostudies-literature"],"pubmed_title":["Isoform-level gene expression patterns in single-cell RNA-sequencing data."],"pmcid":["PMC6041805"],"pubmed_authors":["Pawitan Y","Wills QF","Niu N","Kalari KR","Vu TN","Rantalainen M","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Isoform-level gene expression patterns in single-cell RNA-sequencing data.","description":"Motivation: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. Results: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, ","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Jul","modification":"2025-04-04T18:41:33.421Z","creation":"2019-03-26T23:46:39Z"},"accession":"S-EPMC6041805","cross_references":{"pubmed":["29490015"],"doi":["10.1093/bioinformatics/bty100"]}}