Contents

1 Introduction

This package is designed for reactome pathway-based analysis. Reactome is an open-source, open access, manually curated and peer-reviewed pathway database.

2 Citation

If you use ReactomePA1 in published research, please cite:

G Yu, QY He*. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479. doi:[10.1039/C5MB00663E](http://dx.doi.org/10.1039/C5MB00663E)

3 Supported organisms

Currently ReactomePA supports several model organisms, including ‘celegans’, ‘fly’, ‘human’, ‘mouse’, ‘rat’, ‘yeast’ and ‘zebrafish’. The input gene ID should be Entrez gene ID. We recommend using clusterProfiler::bitr to convert biological IDs. For more detail, please refer to bitr: Biological Id TranslatoR.

4 Pathway Enrichment Analysis

Enrichment analysis is a widely used approach to identify biological themes. Here, we implement hypergeometric model to assess whether the number of selected genes associated with reactome pathway is larger than expected. The p values were calculated based the hypergeometric model2.

library(ReactomePA)
data(geneList)
de <- names(geneList)[abs(geneList) > 1.5]
head(de)
## [1] "4312"  "8318"  "10874" "55143" "55388" "991"
x <- enrichPathway(gene=de,pvalueCutoff=0.05, readable=T)
head(as.data.frame(x))
##                          ID                             Description
## R-HSA-68877     R-HSA-68877                    Mitotic Prometaphase
## R-HSA-2500257 R-HSA-2500257 Resolution of Sister Chromatid Cohesion
## R-HSA-5663220 R-HSA-5663220            RHO GTPases Activate Formins
## R-HSA-68882     R-HSA-68882                        Mitotic Anaphase
## R-HSA-2555396 R-HSA-2555396          Mitotic Metaphase and Anaphase
## R-HSA-2467813 R-HSA-2467813         Separation of Sister Chromatids
##               GeneRatio   BgRatio       pvalue     p.adjust       qvalue
## R-HSA-68877      25/307 128/10281 4.488951e-14 3.070443e-11 2.665020e-11
## R-HSA-2500257    23/307 120/10281 7.339950e-13 2.510263e-10 2.178806e-10
## R-HSA-5663220    21/307 133/10281 3.440692e-10 7.844777e-08 6.808948e-08
## R-HSA-68882      24/307 196/10281 3.839792e-09 5.822015e-07 5.053273e-07
## R-HSA-2555396    24/307 197/10281 4.255859e-09 5.822015e-07 5.053273e-07
## R-HSA-2467813    23/307 185/10281 6.108439e-09 6.963620e-07 6.044139e-07
##                                                                                                                                                              geneID
## R-HSA-68877   CDCA8/CDC20/CENPE/CCNB2/NDC80/NCAPH/SKA1/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/NCAPG/AURKB/CCNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-2500257             CDCA8/CDC20/CENPE/CCNB2/NDC80/SKA1/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/CCNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-5663220                          CDCA8/CDC20/CENPE/NDC80/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1/EVL
## R-HSA-68882        CDCA8/CDC20/CENPE/NDC80/UBE2C/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/PTTG1/LMNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/TAOK1
## R-HSA-2555396      CDCA8/CDC20/CENPE/NDC80/UBE2C/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/PTTG1/LMNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/TAOK1
## R-HSA-2467813            CDCA8/CDC20/CENPE/NDC80/UBE2C/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/PTTG1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/TAOK1
##               Count
## R-HSA-68877      25
## R-HSA-2500257    23
## R-HSA-5663220    21
## R-HSA-68882      24
## R-HSA-2555396    24
## R-HSA-2467813    23

For calculation/parameter details, please refer to the vignette of DOSE3..

4.1 Pathway analysis of NGS data

Pathway analysis using NGS data (eg, RNA-Seq and ChIP-Seq) can be performed by linking coding and non-coding regions to coding genes via ChIPseeker package, which can annotates genomic regions to their nearest genes, host genes, and flanking genes respectivly. In addtion, it provides a function, seq2gene, that simultaneously considering host genes, promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function maps genomic regions to genes in a many-to-many manner and facilitate functional analysis. For more details, please refer to ChIPseeker4.

4.2 Visualize enrichment result

We implement barplot, dotplot enrichment map and category-gene-network for visualization. It is very common to visualize the enrichment result in bar or pie chart. We believe the pie chart is misleading and only provide bar chart.

barplot(x, showCategory=8)

dotplot(x, showCategory=15)

Enrichment map can be viusalized by enrichMap:

enrichMap(x, layout=igraph::layout.kamada.kawai, vertex.label.cex = 1)

In order to consider the potentially biological complexities in which a gene may belong to multiple annotation categories, we developed cnetplot function to extract the complex association between genes and diseases.

cnetplot(x, categorySize="pvalue", foldChange=geneList)