Jan 16, 2016

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

BioRxiv : the Preprint Server for Biology
Trung Nghia VuMattias Rantalainen

Abstract

RNA-sequencing of single-cells enables characterization of transcriptional heterogeneity in seemingly homogenous cell populations. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of pairs of isoforms from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and introduce the concept of differential pattern analysis. We applied ISOP for analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in two independent datasets. In the primary dataset we detected and assigned pattern type of 16562 isoform-pairs from 4929 genes. Our results showed that 78% of the isoform pairs displayed a mutually exclusive expression pattern, 14% of the isoform pairs displayed bimodal isoform preference and 8% isoform pairs displayed isoform preference. 26% of the isoform-pair patterns were significant, while remaining isoform-pair patterns can be understood as effects of transcriptional bursting, drop-out and biological heterogeneity. 32% of genes discovered through differential pattern analysis were novel and not detected by differential expression...Continue Reading

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Mentioned in this Paper

Study
Patterns
Genes
Sequence Determinations, RNA
Transcription, Genetic
Virus Replication
Homogeneous Organ
Gene Expression
Differential Diagnosis
Human RNA Sequencing

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