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

Bioinformatics
Trung Nghia VuMattias Rantalainen

Abstract

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. 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 ...Continue Reading

References

Sep 9, 2020·PLoS Computational Biology·Wei Xiong WenSupat Thongjuea

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Related Concepts

Study
Patterns
Genes
Sequence Determinations, RNA
Transcription, Genetic
Virus Replication
Bio-Informatics
Homogeneous Organ
Gene Expression
Differential Diagnosis

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