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

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 isoformlevel 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, dropout and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differentialpattern 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.

Original languageEnglish (US)
Pages (from-to)2392-2400
Number of pages9
JournalBioinformatics
Volume34
Issue number14
DOIs
StatePublished - Jan 1 2018

Fingerprint

RNA Sequence Analysis
RNA
Gene expression
Sequencing
Gene Expression
Protein Isoforms
Genes
Cell
Cells
Availability
Drop out
Gene
Single-Cell Analysis
Mixture Modeling
Pattern Analysis
Bursting
Differential Expression
Cell Population
Licensure
Breast Cancer

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Isoform-level gene expression patterns in single-cell RNA-sequencing data. / Vu, Trung Nghia; Wills, Quin F.; Kalari, Krishna R; Niu, Nifang; Wang, Liewei M; Pawitan, Yudi; Rantalainen, Mattias.

In: Bioinformatics, Vol. 34, No. 14, 01.01.2018, p. 2392-2400.

Research output: Contribution to journalArticle

Vu, Trung Nghia ; Wills, Quin F. ; Kalari, Krishna R ; Niu, Nifang ; Wang, Liewei M ; Pawitan, Yudi ; Rantalainen, Mattias. / Isoform-level gene expression patterns in single-cell RNA-sequencing data. In: Bioinformatics. 2018 ; Vol. 34, No. 14. pp. 2392-2400.
@article{a7056ee5c21546f698173db03d179327,
title = "Isoform-level gene expression patterns in single-cell RNA-sequencing data",
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 isoformlevel 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, dropout and stochastic biological heterogeneity. Furthermore, 32{\%} of genes discovered through differentialpattern 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.",
author = "Vu, {Trung Nghia} and Wills, {Quin F.} and Kalari, {Krishna R} and Nifang Niu and Wang, {Liewei M} and Yudi Pawitan and Mattias Rantalainen",
year = "2018",
month = "1",
day = "1",
doi = "10.1093/bioinformatics/bty100",
language = "English (US)",
volume = "34",
pages = "2392--2400",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "14",

}

TY - JOUR

T1 - Isoform-level gene expression patterns in single-cell RNA-sequencing data

AU - Vu, Trung Nghia

AU - Wills, Quin F.

AU - Kalari, Krishna R

AU - Niu, Nifang

AU - Wang, Liewei M

AU - Pawitan, Yudi

AU - Rantalainen, Mattias

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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 isoformlevel 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, dropout and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differentialpattern 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.

AB - 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 isoformlevel 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, dropout and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differentialpattern 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.

UR - http://www.scopus.com/inward/record.url?scp=85053445257&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053445257&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bty100

DO - 10.1093/bioinformatics/bty100

M3 - Article

C2 - 29490015

AN - SCOPUS:85053445257

VL - 34

SP - 2392

EP - 2400

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 14

ER -