rSeqDiff: Detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test

Yang Shi, Hui Jiang

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

High-throughput sequencing of transcriptomes (RNA-Seq) has recently become a powerful tool for the study of gene expression. We present rSeqDiff, an efficient algorithm for the detection of differential expression and differential splicing of genes from RNA-Seq experiments across multiple conditions. Unlike existing approaches which detect differential expression of transcripts, our approach considers three cases for each gene: 1) no differential expression, 2) differential expression without differential splicing and 3) differential splicing. We specify statistical models characterizing each of these three cases and use hierarchical likelihood ratio test for model selection. Simulation studies show that our approach achieves good power for detecting differentially expressed or differentially spliced genes. Comparisons with competing methods on two real RNA-Seq datasets demonstrate that our approach provides accurate estimates of isoform abundances and biological meaningful rankings of differentially spliced genes. The proposed approach is implemented as an R package named rSeqDiff.

Original languageEnglish (US)
Article numbere79448
JournalPLoS One
Volume8
Issue number11
DOIs
StatePublished - Nov 18 2013
Externally publishedYes

Fingerprint

RNA Isoforms
Recombinant DNA
Protein Isoforms
RNA
RNA Splicing
High-Throughput Nucleotide Sequencing
Statistical Models
Genes
Transcriptome
genes
testing
Gene Expression
Gene expression
statistical models
transcriptomics
Throughput
gene expression
Experiments
Datasets

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

rSeqDiff : Detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test. / Shi, Yang; Jiang, Hui.

In: PLoS One, Vol. 8, No. 11, e79448, 18.11.2013.

Research output: Contribution to journalArticle

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