Isoform-level gene expression patterns in single-cell RNA-sequencing data

• Published in: Bioinformatics Vol. 34; no. 14; pp. 2392 - 2400
• Main Authors: Vu, Trung Nghia, Wills, Quin F, Kalari, Krishna R, Niu, Nifang, Wang, Liewei, Pawitan, Yudi, Rantalainen, Mattias
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.07.2018
• Subjects: Breast Neoplasms - genetics; Cell Line, Tumor; Female; Gene Expression; Gene Expression Profiling - methods; Humans; Medicin och hälsovetenskap; Original Papers; RNA Isoforms - genetics; Sequence Analysis, RNA - methods; Software
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bty100

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, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<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. Availability and implementation The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. Supplementary information Supplementary data are available at Bioinformatics online.