Travis-CI Build Status codecov [CRAN_Status_Badge]


Pathway analysis for alternative splicing in RNA-seq datasets that accounts for different number of gene features


In alternative splicing ananlysis of RNASeq data, one popular approach is to first identify gene features (e.g. exons or junctions) significantly associated with splicing using methods such as DEXSeq [Anders2012] or JunctionSeq [Hartley2016], and then perform pathway analysis based on the list of genes associated with the significant gene features.

For DEXSeq results, we use gene features to refers to non-overlapping exon counting bins [Anders2012, Figure 1], while for JunctionSeq results, gene features refers to non-overlapping exon or splicing junction counting bins.

A major challenge is that without explicit adjustment, pathways analysis would be biased toward pathways that include genes with a large number of gene features, because these genes are more likely to be selected as "significant genes" in pathway analysis.

PathwaySplice is an R package that falicitate the folowing analysis:

  1. Performing pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene;
  2. Visualizing selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression;
  3. Supporting gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets;
  4. Identifing the significant genes driving pathway significance and
  5. Organizing significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph.


The latest version installed by visiting Bioconductor website

or by ```{r eval=FALSE, message=FALSE, warning=FALSE, results='hide'} library(devtools) install_github("SCCC-BBC/PathwaySplice",ref = 'development')

After installation, the PathwaySplice package can be loaded into R using:
```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide'}


Yan A, Ban Y, Gao Z, Chen X, Wang L. 2017. PathwaySplice: An R package for unbiased pathway analysis of alternative splicing in RNA-Seq data. Submitted

Anders, Simon, Alejandro Reyes, and Wolfgang Huber. 2012. “Detecting differential usage of exons from RNA-seq data.” Genome Research 22 (10): 2008–17. doi:10.1101/gr.133744.111.

Hartley, Stephen W., and James C. Mullikin. 2016. “Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq.” Nucleic Acids Research, June. Oxford University Press, gkw501. doi:10.1093/nar/gkw501.

Shannon, P., Andrew Markiel, Owen Ozier, Nitin S Baliga, Jonathan T Wang, Daniel Ramage, Nada Amin, Benno Schwikowski, and Trey Ideker. 2003. “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.” Genome Research 13 (11): 2498–2504. doi:10.1101/gr.1239303.

Young, Matthew D., Matthew J. Wakefield, Gordon K. Smyth, and Alicia Oshlack. 2010. “Gene Ontology Analysis for Rna-Seq: Accounting for Selection Bias.” Genome Biology 11 (2): R14. doi:10.1186/gb-2010-11-2-r14.

Try the PathwaySplice package in your browser

Any scripts or data that you put into this service are public.

PathwaySplice documentation built on April 28, 2020, 7:44 p.m.