| Literature DB >> 29688305 |
Aimin Yan1, Yuguang Ban1, Zhen Gao1, Xi Chen1,2, Lily Wang1,2,3.
Abstract
Summary: 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. Availability and implementation: https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html. Supplementary information: Supplementary data are available at Bioinformatics online.Mesh:
Year: 2018 PMID: 29688305 PMCID: PMC6137985 DOI: 10.1093/bioinformatics/bty317
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937