PathwaySplice: An R package for unbiased pathway analysis of alternative splicing in RNA-Seq data

Aimin Yan, Yuguang Ban, Zhen Gao, Xi Chen, Lily Wang

Research output: Contribution to journalArticle

Abstract

Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the ‘significant’ gene list in alternative splicing. We present PathwaySplice, an R package that (i) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (ii) visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (iii) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (iv) identifies the significant genes driving pathway significance and (v) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph.

Original languageEnglish (US)
Pages (from-to)3220-3222
Number of pages3
JournalBioinformatics
Volume34
Issue number18
DOIs
StatePublished - 2018

ASJC Scopus subject areas

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

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