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

Trung Nghia Vu; Quin F Wills; Krishna R Kalari; Nifang Niu; Liewei Wang; Yudi Pawitan; Mattias Rantalainen

doi:10.1093/bioinformatics/bty100

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

Journal article

Open access

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

Trung Nghia Vu, Quin F Wills, Krishna R Kalari, Nifang Niu, Liewei Wang, Yudi Pawitan and Mattias Rantalainen

Bioinformatics, Vol.34(14), pp.2392-2400

07/15/2018

DOI: 10.1093/bioinformatics/bty100

PMCID: PMC6041805

PMID: 29490015

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Abstract

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.

Details

Title: Subtitle: Isoform-level gene expression patterns in single-cell RNA-sequencing data
Creators: Trung Nghia Vu - Karolinska Institutet
Quin F Wills - Novo Nordisk
Krishna R Kalari - Department of Health Sciences Research
Nifang Niu - Mayo Clinic
Liewei Wang - Mayo Clinic
Yudi Pawitan - Karolinska Institutet
Mattias Rantalainen - Karolinska Institutet
Resource Type: Journal article
Publication Details: Bioinformatics, Vol.34(14), pp.2392-2400
Publisher: Oxford University Press
DOI: 10.1093/bioinformatics/bty100
PMID: 29490015
PMCID: PMC6041805
ISSN: 1367-4803
eISSN: 1460-2059
Number of pages: 9
Grant note: Swedish Cancer Society (10.13039/501100002794) SERC (10.13039/100004815) Swedish e-Science Research Centre Swedish Research Council (10.13039/501100004359)
Language: English
Date published: 07/15/2018
Academic Unit: Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984701550302771

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