RSeqNP: A non-parametric approach for detecting differential expression and splicing from RNA-Seq data

Yang Shi, Arul M. Chinnaiyan, Hui Jiang

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Summary: High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. Availability and implementation: The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/.

Original languageEnglish (US)
Pages (from-to)2222-2224
Number of pages3
JournalBioinformatics
Volume31
Issue number13
DOIs
StatePublished - Jul 1 2015
Externally publishedYes

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ASJC Scopus subject areas

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

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