This package is designed for reactome pathway-based analysis. Reactome is an open-source, open access, manually curated and peer-reviewed pathway database.
If you use Reactome1 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)
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.
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 GeneRatio BgRatio
## 68877 68877 Mitotic Prometaphase 25/248 100/6748
## 69278 69278 Cell Cycle, Mitotic 49/248 407/6748
## 2500257 2500257 Resolution of Sister Chromatid Cohesion 23/248 92/6748
## 1640170 1640170 Cell Cycle 54/248 496/6748
## 5663220 5663220 RHO GTPases Activate Formins 21/248 102/6748
## 68886 68886 M Phase 30/248 235/6748
## pvalue p.adjust qvalue
## 68877 8.570808e-15 4.473962e-12 3.951593e-12
## 69278 4.338551e-14 1.132362e-11 1.000150e-11
## 2500257 1.072091e-13 1.589955e-11 1.404316e-11
## 1640170 1.218356e-13 1.589955e-11 1.404316e-11
## 5663220 7.285769e-11 7.606343e-09 6.718246e-09
## 68886 1.545857e-09 1.344895e-07 1.187869e-07
## geneID
## 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
## 69278 CDC45/CDCA8/MCM10/CDC20/FOXM1/KIF23/CENPE/MYBL2/CCNB2/NDC80/TOP2A/NCAPH/RRM2/UBE2C/SKA1/NEK2/CENPM/CENPN/CCNA2/CDK1/ERCC6L/MAD2L1/GINS1/KIF18A/CDT1/BIRC5/NCAPG/AURKB/GINS2/KIF20A/AURKA/CCNB1/MCM5/PTTG1/MCM2/KIF2C/CDC25A/CDC6/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/CCNE1/ORC6/ORC1/TAOK1
## 2500257 CDCA8/CDC20/CENPE/CCNB2/NDC80/SKA1/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/CCNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## 1640170 CDC45/CDCA8/MCM10/CDC20/FOXM1/KIF23/CENPE/MYBL2/CCNB2/NDC80/TOP2A/NCAPH/RRM2/UBE2C/HJURP/SKA1/NEK2/CENPM/CENPN/CCNA2/CDK1/ERCC6L/MAD2L1/GINS1/KIF18A/CDT1/BIRC5/NCAPG/AURKB/GINS2/CHEK1/KIF20A/AURKA/CCNB1/MCM5/PTTG1/LMNB1/MCM2/KIF2C/CDC25A/CDC6/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/RAD51/CCNE1/ORC6/ORC1/OIP5/TAOK1
## 5663220 CDCA8/CDC20/CENPE/NDC80/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1/EVL
## 68886 CDCA8/CDC20/KIF23/CENPE/CCNB2/NDC80/NCAPH/UBE2C/SKA1/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/NCAPG/AURKB/KIF20A/CCNB1/PTTG1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/ESPL1/TAOK1
## Count
## 68877 25
## 69278 49
## 2500257 23
## 1640170 54
## 5663220 21
## 68886 30
For calculation/parameter details, please refer to the vignette of DOSE3..
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.
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)